Data loss prevention system for cloud security based on document discourse analysis

12141,177

17/338295

June 03, 2021

Systems, devices, and methods of the present invention are related to determining a document classification. For example, a document classification application generates a set of discourse trees, each discourse tree corresponding to a sentence of a document and including a rhetorical relationship that relates two elementary discourse units. The document classification application creates one or more communicative discourse trees from the discourse trees by matching each elementary discourse unit in a discourse tree that has a verb to a verb signature. The document classification application combines the first communicative discourse tree and the second communicative discourse tree into a parse thicket and applies a classification model to the parse thicket in order to determine whether the document is public or private.

Oracle International Corporation (Redwood Shores, CA, US)

Oracle International Corporation (Redwood Shores, CA, US)

1. A method for classifying documents, the method comprising: generating a discourse tree from a sentence of a document, wherein the discourse tree comprises elementary discourse units, comprises a rhetorical relationship that relates two of the elementary discourse units, and comprises a plurality of nodes, each nonterminal node of the nodes of the discourse tree representing one of the rhetorical relationships and each terminal node of the nodes of the discourse tree associated with one of the elementary discourse units; creating a communicative discourse tree from the discourse tree by matching, in the discourse tree, each elementary discourse unit that has a verb to a verb signature, wherein each verb signature comprises a respective verb of a respective elementary discourse unit and a sequence of thematic roles, and wherein each thematic role describes a relationship between the verb and related words; combining the communicative discourse tree into a parse thicket that comprises discourse relationships between the sentences represented by the communicative discourse tree; applying a machine learning model to the parse thicket to determine a classification, wherein the machine learning model is trained to classify text as public or private based on discourse relationships; and providing the classification to a user interface.

2. The method of claim 1 , wherein applying the machine learning model comprises generating a feature vector from the parse thicket and providing the feature vector to the machine learning model.

3. The method of claim 1 , wherein the machine learning model is a support vector machine (SVM) and uses tree kernel learning.

4. The method of claim 1 , wherein the document is trained classified using one or more rules that specify individuals with access to the document and wherein the machine learning model determines the classification based on the one or more rules.

5. The method of claim 1 , further comprising: determining a set of keywords from the document; executing a query for the document, wherein the query comprises the set of keywords; and responsive to receiving a result of the query that indicates that the document is public, updating the classification to public.

6. The method of claim 1 , further comprising one or more of: (i) responsive to determining that the document is classified as public, permitting a transmission of the document over a data network or (ii) responsive to determining that the document is classified as private, preventing the transmission of the document over the data network.

7. The method of claim 1 , further comprising: accessing a set of training data comprising a set of training data pairs, wherein each training data pair comprises a training parse thicket corresponding to training sentences from a training document and a respective classification and wherein the set of training data includes (i) a first training data pair that has the respective classification of private and (ii) a second training data pair that has the respective classification of public; and training the machine learning model by iteratively: providing one of the training data pairs to the machine learning model; receiving, from the machine learning model, a determined classification; calculating a loss function by calculating a difference between the determined classification and the respective classification; and adjusting internal parameters of the machine learning model to minimize the loss function.

8. A system comprising: a non-transitory computer-readable medium storing computer-executable program instructions; and a processing device communicatively coupled to the non-transitory computer-readable medium for executing the computer-executable program instructions, wherein executing the computer-executable program instructions configures the processing device to perform operations comprising: generating a discourse tree from a sentence of a document, wherein the discourse tree comprises elementary discourse units, comprises a rhetorical relationship that relates two of the elementary discourse units, and comprises a plurality of nodes, each nonterminal node of the nodes of the discourse tree representing one of the rhetorical relationships and each terminal node of the nodes of the discourse tree associated with one of the elementary discourse units; creating a communicative discourse tree from the discourse tree by matching, in the discourse tree, each elementary discourse unit that has a verb to a verb signature, wherein each verb signature comprises a respective verb of a respective elementary discourse unit and a sequence of thematic roles, and wherein each thematic role describes a relationship between the verb and related words; combining the communicative discourse tree into a parse thicket that comprises discourse relationships between the sentences represented by the communicative discourse tree; applying a machine learning model to the parse thicket to determine a classification, wherein the machine learning model is trained to classify text as public or private based on discourse relationships; and providing the classification to an external device.

9. The system of claim 8 , wherein applying the machine learning model comprises generating a feature vector from the parse thicket and providing the feature vector to the machine learning model.

10. The system of claim 8 , wherein the machine learning model is a support vector machine (SVM) and uses tree kernel learning.

11. The system of claim 8 , wherein the document is classified using one or more rules that specify individuals with access to the document and wherein the machine learning model determines the classification based on the one or more rules.

12. The system of claim 8 , wherein executing the computer-executable program instructions further configures the processing device to perform operations comprising: determining a set of keywords from the document; executing a query for the document, wherein the query comprises the set of keywords; and responsive to receiving a result of the query that indicates that the document is public, updating the classification to public.

13. The system of claim 8 , wherein executing the computer-executable program instructions further configures the processing device to perform operations comprising one or more of: (i) responsive to determining that the document is classified as public, permitting a transmission of the document over a data network or (ii) responsive to determining that the document is classified as private, preventing the transmission of the document over the data network.

14. The system of claim 8 , wherein executing the computer-executable program instructions further configures the processing device to perform operations comprising: accessing a set of training data comprising a set of training data pairs, wherein each training data pair comprises a training parse thicket corresponding to training sentences from a training document and a respective classification and wherein the set of training data includes (i) a first training data pair that has the respective classification of private and (ii) a second training data pair that has the respective classification of public; and training the machine learning model by iteratively: providing one of the training data pairs to the machine learning model; receiving, from the machine learning model, a determined classification; calculating a loss function by calculating a difference between the determined classification and the respective classification; and adjusting internal parameters of the machine learning model to minimize the loss function.

15. A non-transitory computer-readable medium storing computer-executable program instructions that when executed by a processor, perform operations comprising: generating a discourse tree from a sentence of a document, wherein the discourse tree comprises elementary discourse units, comprises a rhetorical relationship that relates two of the elementary discourse units, and comprises a plurality of nodes, each nonterminal node of the nodes of the discourse tree representing one of the rhetorical relationships and each terminal node of the nodes of the discourse tree associated with one of the elementary discourse units; creating a communicative discourse tree from the discourse tree by matching, in the discourse tree, each elementary discourse unit that has a verb to a verb signature, wherein each verb signature comprises a respective verb of a respective elementary discourse unit and a sequence of thematic roles, and wherein each thematic role describes a relationship between the verb and related words; combining the communicative discourse tree into a parse thicket that comprises discourse relationships between the sentences represented by the communicative discourse tree; applying a machine learning model to the parse thicket to determine a classification, wherein the machine learning model is trained to classify text as public or private based on discourse relationships; and providing the classification to a user interface.

16. The non-transitory computer-readable medium of claim 15 , wherein the machine learning model is a support vector machine (SVM) and uses tree kernel learning.

17. The non-transitory computer-readable medium of claim 15 , wherein the document is classified using one or more rules that specify individuals with access to the document and wherein the machine learning model determines the classification based on the one or more rules.

18. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise: determining a set of keywords from the document; executing a query for the document, wherein the query comprises the set of keywords; and responsive to receiving a result of the query that indicates that the document is public, updating the classification to public.

19. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise one or more of: (i) responsive to determining that the document is classified as public, permitting a transmission of the document over a data network or (ii) responsive to determining that the document is classified as private, preventing the transmission of the document over the data network.

20. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise: accessing a set of training data comprising a set of training data pairs, wherein each training data pair comprises a training parse thicket corresponding to a plurality of training sentences from a training document and a respective classification and wherein the set of training data includes (i) a first training data pair that has the respective classification of private and (ii) a second training data pair that has the respective classification of public; and training the machine learning model by iteratively: providing one of the training data pairs to the machine learning model; receiving, from the machine learning model, a determined classification; calculating a loss function by calculating a difference between the determined classification and the respective classification; and adjusting internal parameters of the machine learning model to minimize the loss function.

5495605 February 1996 Cadot
6112168 August 2000 Corston et al.
6181909 January 2001 Burstein et al.
6411424 June 2002 Raj
6731307 May 2004 Strubbe et al.
6961692 November 2005 Polanyi et al.
7013259 March 2006 Polanyi et al.
7152031 December 2006 Jensen et al.
7519529 April 2009 Horvitz
7840556 November 2010 Dayal et al.
9037464 May 2015 Mikolov et al.
9171037 October 2015 Galitsky et al.
9201860 December 2015 Zhang et al.
9201868 December 2015 Zhang et al.
9292490 March 2016 Kimelfeld et al.
9449080 September 2016 Zhang
9559993 January 2017 Palakovich et al.
9582501 February 2017 Salmon et al.
9620933 April 2017 Huang et al.
10019716 July 2018 Ainslie et al.
10551626 February 2020 Marciante
10599885 March 2020 Galitsky
10679011 June 2020 Galitsky
10796099 October 2020 Galitsky et al.
10796102 October 2020 Galitsky
10817670 October 2020 Galitsky
10839154 November 2020 Galitsky
10853574 December 2020 Galitsky
10853581 December 2020 Galitsky
10935802 March 2021 Marciante
11100144 August 2021 Galitsky
11295085 April 2022 Galitsky
11328016 May 2022 Galitsky
11556698 January 2023 Galitsky
11580298 February 2023 Galitsky
11782985 October 2023 Galitsky
11797773 October 2023 Galitsky
11809825 November 2023 Galitsky
11880652 January 2024 Galitsky
20010007987 July 2001 Igata
20010053968 December 2001 Galitsky et al.
20020040292 April 2002 Marcu
20020046018 April 2002 Marcu et al.
20020107851 August 2002 Beauchamp
20020176151 November 2002 Moon et al.
20030138758 July 2003 Burstein et al.
20040008416 January 2004 Okuno
20040044519 March 2004 Polanyi et al.
20040148170 July 2004 Acero et al.
20040158452 August 2004 Polanyi et al.
20040158453 August 2004 Polanyi et al.
20050086592 April 2005 Polanyi et al.
20050267871 December 2005 Marchisio et al.
20070073533 March 2007 Thione et al.
20070106499 May 2007 Dahlgren et al.
20070136284 June 2007 Cobb et al.
20070143098 June 2007 Van Den Berg et al.
20070192306 August 2007 Papakonstantinou et al.
20070294229 December 2007 Au
20080172409 July 2008 Botros et al.
20080228467 September 2008 Womack et al.
20090089252 April 2009 Galitsky et al.
20090100053 April 2009 Boschee et al.
20090248399 October 2009 Au
20090282019 November 2009 Galitsky et al.
20100169359 July 2010 Barrett et al.
20110119049 May 2011 Ylonen
20110153673 June 2011 Boschee et al.
20110216792 September 2011 Chann et al.
20110282856 November 2011 Ganti et al.
20120041950 February 2012 Koll et al.
20120078902 March 2012 Duboue et al.
20120254333 October 2012 Chandramouli et al.
20130046757 February 2013 Salvetti et al.
20130103386 April 2013 Zhang et al.
20130204611 August 2013 Tsuchida et al.
20140040288 February 2014 Galitsky
20140114649 April 2014 Zuev et al.
20140122083 May 2014 Xiaojiang
20140136188 May 2014 Wroczynski et al.
20150039294 February 2015 Howald et al.
20150039295 February 2015 Soschen
20150046492 February 2015 Balachandran
20150051900 February 2015 Kimelfeld et al.
20150081277 March 2015 Behi
20150149461 May 2015 Aguilar Lemarroy et al.
20150161200 June 2015 Barba et al.
20150161512 June 2015 Byron et al.
20150262393 September 2015 Bliss et al.
20150278198 October 2015 Andreev
20160034457 February 2016 Bradley et al.
20160055240 February 2016 Tur et al.
20160085743 March 2016 Haley
20160086601 March 2016 Chotimongkol et al.
20160098394 April 2016 Bruno et al.
20160099892 April 2016 Palakovich et al.
20160232152 August 2016 Mahamood
20160245779 August 2016 Khalaj Amineh et al.
20160246779 August 2016 Ho et al.
20160247068 August 2016 Lin
20160275573 September 2016 L'Huillier et al.
20160292153 October 2016 Agarwalla et al.
20160371321 December 2016 Myslinski
20170032053 February 2017 LeTourneau
20170060831 March 2017 Smythe et al.
20170082863 March 2017 Marciante
20170116982 April 2017 Gelfenbeyn et al.
20170228368 August 2017 Carter et al.
20170286390 October 2017 Yashpe et al.
20170358295 December 2017 Roux et al.
20180052818 February 2018 Bethard et al.
20180113939 April 2018 Chen et al.
20180181648 June 2018 Chen
20180189385 July 2018 Sun et al.
20180314689 November 2018 Wang et al.
20180329880 November 2018 Galitsky
20180357221 December 2018 Galitsky
20180365228 December 2018 Galitsky
20180365593 December 2018 Galitsky
20180373701 December 2018 McAteer et al.
20190005027 January 2019 He et al.
20190057157 February 2019 Mandal et al.
20190095420 March 2019 Galitsky
20190103111 April 2019 Tiwari et al.
20190163756 May 2019 Bull et al.
20190295537 September 2019 Sapugay et al.
20190354544 November 2019 Hertz et al.
20190370604 December 2019 Galitsky
20190371299 December 2019 Jiang et al.
20200012720 January 2020 Elson et al.
20200151777 May 2020 Casalino et al.
1647070 July 2005
102165518 August 2011
105765566 July 2016
106021224 October 2016
106354844 January 2017
106383875 February 2017
2001-523019 November 2001
2004139446 May 2004
2004524559 August 2004
2005-122743 May 2005
2006260597 September 2006
2015225415 December 2015
2017224155 December 2017
2018041336 March 2018
9921104 April 1999
2015003143 January 2015

U.S. Appl. No. 17/690,897 , “Notice of Allowance”, Jun. 12, 2023, 7 pages. cited by applicant
EP19727786.6 , “Office Action”, Apr. 21, 2023, 7 pages. cited by applicant
U.S. Appl. No. 18/151,164, Notice of Allowance mailed on Nov. 28, 2023, 8 pages. cited by applicant
U.S. Appl. No. 17/235,329, Non-Final Office Action mailed on Apr. 13, 2023, 26 pages. cited by applicant
U.S. Appl. No. 17/652,366, Non-Final Office Action mailed on Apr. 17, 2023, 13 pages. cited by applicant
U.S. Appl. No. 17/690,897, Non-Final Office Action mailed on Mar. 30, 2023, 15 pages. cited by applicant
Chiarcos et al., Rhetorical Distance Revisited: a Parameterized Approach, Pragmatics & Beyond New Series 172, 2008, pp. 97-115. cited by applicant
Chinese Application No. 201880062996.5, Office Action mailed on Mar. 2, 2023, 17 pages. (9 pages of English Translation and 8 pages of Original Document). cited by applicant
Japanese Application No. 2022-191911, Office Action mailed on Jan. 30, 2024, 3 pages. cited by applicant
Data Loss Prevention & Protection, Symantec, Available Online at: http://www.symantec.com/business/theme.jsp?themeid=vontu, Accessed from Internet on Aug. 30, 2018, 6 pages. cited by applicant
Exploring Dialog Management for Bots, Chatbots Magazine, Available Online at: https://chatbotsmagazine.com/exploring-dialog management-for-bots-cbb8665a2fd3, Jul. 11, 2016, 7 pages. cited by applicant
Frase HubSpot Assistant, Available online at: https://www.frase.io/?hubspot, Accessed from Internet on Feb. 19, 2021, 12 pages. cited by applicant
Ignore, Deny, Downplay: Accounts of Syrians from Douma Have No Place in Western Narrative, Russia Today, Available Online at: https://www.rt.com/news/425438-douma-witnesses-gas-attack-syria/, Apr. 28, 2018, pp. 1-8. cited by applicant
Language Data, Yahoo Labs, Available Online at: https://webscope.sandbox.yahoo.corn/catalog.php?datatype=I&guccounter=1, Accessed from Internet on Aug. 19, 2019, 5 pages. cited by applicant
Sense2vec: Semantic Analysis of the Reddit Hivemind, ExplosionAI, Available online at: https://explosion.ai/demos/sense2vec, 2019, 10 pages. cited by applicant
Start by Selecting a Make, Available Online at: https://web.archive.org/web/20170823095844/https://www.2carpros.com/, Accessed from Internet on Oct. 14, 2019, 6 pages. cited by applicant
Suicide Bomber Trial: Emails in Full, BBC News, Available Online at: news.bbc.co.uk/1/hi/uk/ 3825765.stm, Nov. 28, 2005, pp. 1-4. cited by applicant
Task Oriented Dialogue Dataset Survey, AtmaHou, Available online at https://github.com/AtmaHou/Task-Oriented-Dialogue-Dataset-Survey, Accessed from Internet on Feb. 2, 2021, 18 pages. cited by applicant
The bAbI Project, Facebook Babi, Available Online at: https://research.fb.com/downloads/babi/, 2019, 6 pages. cited by applicant
Trump Russia Affair: Key Questions Answered, BBC News, Available Online at: http://www.bbc.com/news/world-us-canada-42493918, Jul. 13, 2018, pp. 1-21. cited by applicant
Turku NLP Group, Available Online at: http://bionlp-www.utu.fi/wv_demo/, Accessed from Internet on Nov. 4, 2021, 2 pages. cited by applicant
Vietnamese Police Detain 8 Suspects in Connection with Illegal Immigration Organizing, People.cn, Available Online at: http://en.people.cn/n3/2019/1104/c90000-9629296.html, Nov. 4, 2019, 1 page. cited by applicant
Welcome to Apache Lucene, Apache Lucene 7.5.0 and Apache Solr 7.5.0, Available Online at: www.lucene.apache.org, Sep. 24, 2018, 38 pages. cited by applicant
Word to Vec JS Demo, Turbomaze, Available Online at: http://turbomaze.github.io/word2vecjson/, Accessed from Internet on Nov. 4, 2021, 1 page. cited by applicant
U.S. Appl. No. 16/010,123, Non-Final Office Action mailed on Feb. 8, 2021, 30 pages. cited by applicant
U.S. Appl. No. 16/010,123, Notice of Allowance mailed on May 19, 2021, 16 pages. cited by applicant
U.S. Appl. No. 16/145,644, Non-Final Office Action Mailed on Apr. 7, 2020, 17 pages. cited by applicant
U.S. Appl. No. 16/145,644, Notice of Allowance mailed on Jul. 16, 2020, 10 pages. cited by applicant
U.S. Appl. No. 16/145,702, Corrected Notice of Allowability mailed on Sep. 4, 2020, 3 pages. cited by applicant
U.S. Appl. No. 16/145,702, Final Office Action mailed on Sep. 10, 2019, 25 pages. cited by applicant
U.S. Appl. No. 16/145,702, Non-Final Office Action mailed on Feb. 5, 2020, 30 pages. cited by applicant
U.S. Appl. No. 16/145,777, Supplemental Notice of Allowability mailed on Sep. 2, 2020, 12 pages. cited by applicant
U.S. Appl. No. 16/240,23, Final Office Action mailed on Oct. 21, 2021, 13 pages. cited by applicant
U.S. Appl. No. 16/240,232, Non-Final Office Action mailed on Apr. 9, 2021, 13 pages. cited by applicant
U.S. Appl. No. 16/240,232, Non-Final Office Action mailed on Jan. 4, 2022, 15 pages. cited by applicant
U.S. Appl. No. 16/408,224, Notice of Allowance mailed on Jan. 7, 2022, 9 pages. cited by applicant
U.S. Appl. No. 16/736,517, Non-Final Office Action mailed on Dec. 8, 2021, 17 pages. cited by applicant
U.S. Appl. No. 16/995,302, Notice of Allowance mailed on Jan. 7, 2022, 11 pages. cited by applicant
Abbott et al., Internet Argument Corpus 2.0: An SQL Schema for Dialogic Social Media and the Corpora to Go with it, In Language Resources and Evaluation Conference, 2016, pp. 4445-4452. cited by applicant
Ajjour et al., Unit Segmentation of Argumentative Texts, Proceedings of the 4th Workshop on Argument Mining, Sep. 8, 2017, pp. 118-128. cited by applicant
Aker et al., What Works and What Does Not: Classifier and Feature Analysis for Argument Mining, Proceedings of the 4th Workshop on Argument Mining, Sep. 8, 2017, pp. 91-96. cited by applicant
Alicke et al., Hypocrisy: What counts? Philosophical Psychology, vol. 26, No. 5, Oct. 2013, 30 pages. cited by applicant
Allan, Automatic Hypertext Link Typing, Digital Library, vol. 778, No. 2, Mar. 1996, pp. 42-52. cited by applicant
Anelli et al., Knowledge-Aware and Conversational Recommender Systems, Proceedings of the 12th ACM Conference on Recommender Systems, Sep. 2018, pp. 521-522. cited by applicant
Appel et al., A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level, Knowledge-Based Systems, vol. 108, May 13, 2016, pp. 1-32. cited by applicant
Ariely, Predictably Irrational, 2008, 308 pages. cited by applicant
Arras et al., What is Relevant in a Text Document ?: An Interpretable Machine Learning Approach, Public Library of Science One, Available Online at: https://journals.plos.org/plosone/article/fileid=10.1371/journal.pone.0181142&type=printable, Aug. 11, 2017, 23 pages. cited by applicant
Artooras et al., Stanford NLP-VP vs NP, Stack Overflow Website, Mar. 8-9, 2016, 2 pages. cited by applicant
Arya et al., One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques, ArXiv abs/1909.03012, Sep. 14, 2019, 18 pages. cited by applicant
Auer et al., DBpedia: A Nucleus for a Web of Open Data, International Semantic Web Conference, Lecture Notes in Computer Science, vol. 4825, Nov. 11, 2007, pp. 1-14. cited by applicant
Banarescu et al., Abstract Meaning Representation for Sembanking, In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse., Aug. 8-9, 2013, pp. 178-186. cited by applicant
Banarescu et al., Abstract Meaning Representation for Sembanking, Proceedings of the 7th Linguistic Annotation Workshop & Interoperability with Discourse, Aug. 8-9, 2013, pp. 178-186. cited by applicant
Banchs, Movie-DiC: A Movie Dialogue Corpus for Research and Development, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, vol. 2, Jul. 2012, pp. 203-207. cited by applicant
Bao et al., Knowledge-based Question Answering as Machine Translation, Association for Computational Linguistics, Jun. 23-25, 2014, pp. 967-976. cited by applicant
Barden et al., Saying One Thing and Doing Another: Examining the Impact of Event Order on Hypocrisy Judgments of Others, Personality and Social Psychology, vol. 31, No. 11, Dec. 2005, pp. 1463-1474. cited by applicant
Bar-Haim et al., Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization, Proceedings of the 4th Workshop on Argument Mining, Sep. 8, 2017, pp. 32-38. cited by applicant
Bar-Haim et al., Stance Classification of Context-Dependent Claims, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: vol. 1, Apr. 3-7, 2017, pp. 251-261. cited by applicant
Baroni et al., Argumentation Through a Distributed Self-Stabilizing Approach, Journal of Experimental & Theoretical Artificial Intelligence, vol. 14, No. 4, 2002, pp. 273-301. cited by applicant
Barzilay et al., Modeling Local Coherence: An Entity-Based Approach, Computational Linguistics, vol. 34, No. 1, Mar. 2008, pp. 1-34. cited by applicant
Barzilay et al., Using Lexical Chains for Text Summarization, Proceedings of the ACL/EACL'97 Workshop on Intelligent Scalable Text Summarization, Jul. 1997, pp. 10-17. cited by applicant
Bazinska, Explore Word Analogies, Available Online at: https://lamyiowce.github.io/word2viz/, 2017, 2 Pages. cited by applicant
Bedi et al., Argumentation-Enabled Interest-Based Personalized Recommender System, Journal of Experimental & Theoretical Artificial Intelligence, vol. 27, No. 2, 2015, pp. 1-45. cited by applicant
Bentahar et al., A Taxonomy of Argumentation Models Used for Knowledge Representation, Artificial Intelligence Review, vol. 33, No. 3, Mar. 2010, 49 pages. cited by applicant
Berkovsky et al., Influencing Individually: Fusing Personalization and Persuasion, ACM Transactions on Interactive Intelligent Systems, vol. 2, No. 2, Article 9, Jun. 2012, 8 pages. cited by applicant
Bernard et al., The Power of Well-Connected Arguments: Early Sensitivity to the Connective Because, Journal of Experimental Child Psychology, vol. 111, 2012, pp. 128-135. cited by applicant
Berzlanovich et al., Coherence Structure and Lexical Cohesion in Expository and Persuasive Texts, Proceedings of the Workshop on Constraints in Discourse III, 2008, 8 pages. cited by applicant
Biran et al., Identifying Justifications in Written Dialogs by Classifying Text as Argumentative, International Journal of Semantic Computing, vol. 5, No. 4, Dec. 2011, pp. 363-381. cited by applicant
Bird et al., Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit, Available Online at: http://www.nltk.org/book_1ed, 2009, 504 pages. cited by applicant
Biswa, Putt's Law, Peter Principle, Dilbert Principle of Incompetence & Parkinson's Law, Available Online at: http://asmilingassasin.blogspot.com/2015/06/putts-law-peter-principle-dilbert.html, 2015, 5 pages. cited by applicant
Boguslavsky et al., Multilinguality in ETAP-3: Reuse of Lexical Resources, Proceedings of the Workshop on Multilingual Linguistic Ressources, Aug. 28, 2004, 8 pages. cited by applicant
Bollacker et al., Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, Jun. 2008, pp. 1247-1250. cited by applicant
Bolshakov et al., Synonymous Paraphrasing Using WordNet and Internet, Department of Computer Science and Engineering, Chung-Ang University, Seoul, Jan. 1970, 12 pages. cited by applicant
Bordes et al., Learning End-To-End Goal-Oriented Dialog, Available online at: https://arxiv.org/abs/1605.07683, Mar. 2017, 15 pages. cited by applicant
Bordes et al., Question Answering With Subgraph Embeddings, In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing EMNLP, Oct. 2014, pp. 615-620. cited by applicant
Bowman et al., Large Annotated Corpus for Learning Natural Language Inference, ArXiv preprint arXiv:1508.05326, Aug. 21, 2015, 11 pages. cited by applicant
Bridge, Towards Conversational Recommender Systems: A Dialogue Grammar Approach, Conference: 6th European Conference ov Case Based Reasoning, ECCBR 2002, Jan. 2002, pp. 9-22. cited by applicant
Britt et al., Constructing Representations of Arguments, Journal of Memory and Language, vol. 48, No. 4, 2003, pp. 794-810. cited by applicant
Budanitsky et al., Evaluating WordNet-Based Measures of Lexical Semantic Relatedness, Computational Linguistics, vol. 32, No. 1, Mar. 2006, pp. 13-47. cited by applicant
Budzianowski et al., MultiWOZ—A Large-Scale Multi-DomainWizard-of-Oz Dataset for Task-Oriented Dialogue Modelling, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Oct. 31-Nov. 4, 2018, pp. 5016-5026. cited by applicant
Cabrio et al., A Natural Language Bipolar Argumentation Approach to Support Users in Online Debate Interactions, Argument and Computation, vol. 4, No. 3, 2013, pp. 209-230. cited by applicant
Cabrio et al., Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jul. 8-14, 2012, pp. 208-212. cited by applicant
Camburu et al., e-snli: Natural Language Inference with Natural Language Explanations, Advances in Neural Information Processing Systems, Dec. 6, 2018, 11 pages. cited by applicant
Campbell, Chatbot Win Prize By Changing The Subject, New Scientist, vol. 208, No. 2785, Nov. 6, 2010, 1 page. cited by applicant
Carlson et al., Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory, Proceedings of the Second SIGdial Workshop on Discourse and Dialogue, vol. 16, Sep. 1-2, 2001, 10 pages. cited by applicant
Carreyrou, Hot Startup Theranos Has Struggled with Its Blood-Test Technology, Available Online at: https://www.wsj.com/articles/theranos-has-struggled-with-blood-tests-1444881901, Oct. 16, 2015, 6 pages. cited by applicant
Castellucci et al., Context-Aware Models for Twitter Sentiment Analysis, Emerging Topics at the First Italian Conference on Computational Linguistics, vol. 1, No. 1, Dec. 2015, pp. 75-89. cited by applicant
Chali et al., Complex Question Answering: Unsupervised Learning Approaches and Experiments, Journal of Artificial Intelligence Research, vol. 35, May 2009, pp. 1-47. cited by applicant
Charolles, Cohesion, Coherence Et Pertinence De Discours, Travaux de Linguistique, vol. 29, 1995, pp. 125-151. cited by applicant
Chen et al., Critiquing-Based Recommenders: Survey and Emerging Trends, User Modeling and User-Adapted Interaction, vol. 22, Nos. 1-2, Apr. 2012, pp. 125-150. cited by applicant
Cheng et al., Joint Training for Pivot-Based Neural Machine Translation, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Feb. 21, 2017, 7 pages. cited by applicant
Choi et al., QuAC: Question Answering in Context, Empirical Methods in Natural Language Processing., Aug. 28, 2018, pp. 2174-2184. cited by applicant
Christakopoulou et al., Towards Conversational Recommender Systems, KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, 10 pages. cited by applicant
Collins et al., New Ranking Algorithms for Parsing and Tagging: Kernels Over Discrete Structures, and the Voted Perceptron, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Jul. 2002, pp. 263-270. cited by applicant
Coulmance et al., Trans-Gram, Fast Cross-Lingual Word-Embeddings, Available Online at: https://arxiv.org/pdf/1601.02502.pdf, Jan. 11, 2016, 8 pages. cited by applicant
Craig et al., Overhearing Dialogues and Monologues in Virtual Tutoring Sessions: Effects on Questioning and Vicarious Learning, International Journal of Artificial Intelligence in Education, Jan. 2000, pp. 242-253. cited by applicant
Crutzen et al., An Artificially Intelligent Chat Agent That Answers Adolescents' Questions Related to Sex, Drugs, and Alcohol: An Exploratory Study, Journal of Adolescent Health, vol. 48, No. 5, May 2011, pp. 1-6. cited by applicant
Dagan et al., Recognizing Textual Entailment: Rational, Evaluation and Approaches, Natural Language Engineering, vol. 15, No. 4, Oct. 2009, pp. i-xvii. cited by applicant
Damer, Attacking Faulty Reasoning: A Practical Guide to Fallacy-Free Reasoning, Wadsworth Cengage Learning, 2009, 257 pages. cited by applicant
Damonte et al., An Incremental Parser for Abstract Meaning Representation, Proceedings of Equine Analytical Chemistry Laboratory, Apr. 10, 2017, 12 pages. cited by applicant
Damonte et al., Cross-lingual Abstract Meaning Representation Parsing, Proceedings of NAACL, Feb. 24, 2018, 10 pages. cited by applicant
Das et al., Frame-Semantic Parsing, Computational Linguistics, vol. 40, No. 1, Mar. 2014, pp. 9-56. cited by applicant
De Mori et al., Spoken Language Understanding, Institute of Electrical and Electronics Engineers Signal Processing Magazine, vol. 25, No. 3, May 2008, pp. 50-58. cited by applicant
Devillez, Writing: Step by Step, Kendall Hunt, 2003. cited by applicant
Devlin et al., BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding, In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, May 24, 2019, 16 pages. cited by applicant
Dosilovic et al., Explainable Artificial Intelligence: A Survey, In Proc. MIPRO, May 2018, pp. 210-215. cited by applicant
Du et al., Learning to Ask: Neural Question Generation for Reading Comprehension, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Apr. 29, 2017, 11 pages. cited by applicant
Dung, On the Acceptability of Arguments and Its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and N-Person Games, Artificial Intelligence, vol. 77, No. 2, Sep. 1995, pp. 321-357. cited by applicant
Dunne et al., Computational Models of Argument, Proceedings of Computational Models of Argument, IOS Press, 2006, 353 pages. cited by applicant
Dwivedi, NLP-Building a Question Answering Model, Towards Data Science, Available Online at: https://towardsdatascience.com/nlp-building-a-question-answering-model-ed0529a68c54, Mar. 29, 2018, 7 pages. cited by applicant
Eckle-Kohler et al., On the Role of Discourse Markers for Discriminating Claims and Premises in Argumentative Discourse, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Sep. 17-21, 2015, pp. 2236-2242. cited by applicant
Egg et al., Underspecified Discourse Representation, Constraints in Discourse, 2008, pp. 117-138. cited by applicant
Ellsworth et al., Mutaphrase: Paraphrasing with FrameNet, Proceedings of the Workshop on Textual Entailment and Paraphrasing, Available Online at: http://www.icsi.berkeley.edu/pubs/speech/acl07.pdf, Jun. 2007, pp. 143-150. cited by applicant
Elsner et al., You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement, Proceedings of ACL-08: HLT, Jun. 2008, pp. 834-842. cited by applicant
European Application No. EP18727946.8, Office Action mailed on Dec. 15, 2021, 7 pages. cited by applicant
Fagin et al., Reasoning About Knowledge, The MIT Press, 1996, 75 pages. cited by applicant
Faruqui et al., Improving Vector Space Word Representations Using Multilingual Correlation, Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Apr. 26-30, 2014, pp. 462-471. cited by applicant
Felfernig et al., Developing Constraint-Based Recommenders, Recommender Systems Handbook, 2010, pp. 187-215. cited by applicant
Feng et al., Classifying Arguments by Scheme, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, vol. 1, Jun. 19-24, 2011, pp. 987-996. cited by applicant
Feng et al., Distributional Footprints of Deceptive Product Reviews, Proceedings of the Sixth International Association for the Advancement of Artificial Intelligence Conference on Weblogs and Social Media, The Association for the Advancement of Artificial Intelligence Press, Jan. 2012, pp. 98-105. cited by applicant
Feng et al., Syntactic Stylometry for Deception Detection, In Association for Computational Linguistics 12, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jul. 8-14, 2012, pp. 171-175. cited by applicant
Feng et al., Text-Level Discourse Parsing with Rich Linguistic Features, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, vol. 1, Jul. 8-14, 2012, pp. 60-68. cited by applicant
Feng et al., The Impact of Deep Hierarchical Discourse Structures in the Evaluation of Text Coherence, Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, Aug. 2014, 10 pages. cited by applicant
Ferretti et al., A Possibilistic Defeasible Logic Programming Approach to Argumentation-Based Decision-Making, Journal of Experimental & Theoretical Artificial Intelligence, vol. 26, No. 4, Jun. 10, 2014, pp. 519-550. cited by applicant
Finn, A Question Writing Algorithm, Journal of Reading Behavior, VII, vol. 4, 1975, pp. 341-367. cited by applicant
Flanigan et al., A Discriminative Graph-Based Parser for the Abstract Meaning Representation, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Jun. 2014, pp. 1426-1436. cited by applicant
Florou et al., Argument Extraction for Supporting Public Policy Formulation, Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, Aug. 8, 2013, pp. 49-54. cited by applicant
Foltz et al., The Measurement of Textual Coherence with Latent Semantic Analysis, Discourse Processes, vol. 25, Nos. 2-3, 1998, 31 pages. cited by applicant
Fornaciari et al., Identifying Fake Amazon Reviews as Learning from Crowds, Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Apr. 26-30, 2014, pp. 279-287. cited by applicant
Fouad et al., Meta-agents: Using Multiagent Networks to Manage Dynamic Changes in the Internet of Things, Association for the Advancement of Artificial Intelligence Spring Symposium Series, 2019, 2 pages. cited by applicant
Fox, Discourse Structure and Anaphora: Written and Conversational English, Cambridge University Press, 1987, pp. 77-92. cited by applicant
Freeley et al., Argumentation and Debate, Critical Thinking for Reasoned Decision Making, Eleventh Edition, vol. 27, No. 3, Jun. 10, 1991, pp. 137-152. cited by applicant
Galitsky et al., A Chatbot Demo about a Broken Student, Available Online at: https://drive.google.com/open?id=0B-TymkYCBPsfV3JQSGU3TE9mRVk, 2018. cited by applicant
Galitsky, A Demo of a Chatbot for a Virtual Persuasive Dialogue, Persuasive technologies 14th International Conference, Limassol, Cyprus, Apr. 9-11, 2019. cited by applicant
Galitsky et al., A Novel Approach for Classifying Customer Complaints Through Graphs Similarities in Argumentative Dialogues, Decision Support Systems, vol. 46, No. 3, Nov. 4, 2008, 28 pages. cited by applicant
Galitsky et al., Accessing Validity of Argumentation of Agents of the Internet of Everything, Artificial Intelligence for the Internet of Everything, Chapter 11, Jan. 2019, pp. 187-216. cited by applicant
Galitsky et al., Argumentation in Text: Discourse Structure Matters, Available Online at: https://nlp.fi.muni.cz/trac/research/raw-attachment/wiki/cs/OCemSeMluvi/paper_15.pdf, 2018, 13 pages. cited by applicant
Galitsky, Assuring Chatbot Relevance at Syntactic Level, Developing Enterprise Chatbots, Apr. 2019, 42 pages. cited by applicant
Galitsky, Building Chatbot Thesaurus, Developing Enterprise Chatbots, Apr. 2019, pp. 221-252. cited by applicant
Galitsky et al., Building Dialogue Structure from Discourse Tree of a Question, Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, Oct. 31, 2018, pp. 17-23. cited by applicant
Galitsky et al., Building Integrated Opinion Delivery Environment, Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, Jan. 2011, 6 pages. cited by applicant
Galitsky et al., Building Web Infrastructure for Providing Rating Services and Subscription to Them, Mathematical Modeling, vol. 19, No. 2, 2007, 14 pages. cited by applicant
Galitsky et al., Chatbot Components and Architectures, In Developing Enterprise Chatbots, Springer, 2019, pp. 13-47. cited by applicant
Galitsky et al., Concept-Based Learning of Human Behavior for Customer Relationship Management, Information Sciences, vol. 181, No. 10, May 15, 2011, pp. 2016-2035. cited by applicant
Galitsky et al., Constructing Imaginary Discourse Trees Improves Answering Convergent Questions, CICLING, Apr. 7-13, 2019, 5 pages. cited by applicant
Galitsky et al., Customers' Retention Requires an Explainability Feature in Machine Learning Systems They Use, 2018 AAAI Spring Symposium Series, 2018, pp. 214-220. cited by applicant
Galitsky, Customers Retention Requires an Explainability Feature in Machine Learning Systems They Use, AAAI Spring Symposium on Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-Being AI. Stanford CA, 2018, pp. 214-220. cited by applicant
Galitsky et al., Detecting Logical Argumentation in Text via Communicative Discourse Tree, Journal of Experimental & Theoretical Artificial Intelligence, vol. 30, No. 5, May 2018, 29 pages. cited by applicant
Galitsky et al., Discourse-Based Approach to Involvement of Background Knowledge for Question Answering, Proceedings of the International Conference on Recent Advances in Natural Language Processing, Sep. 2-4, 2019, pp. 373-381. cited by applicant
Galitsky et al., Discourse-Level Dialogue Management, In Developing Enterprise Chatbots: Springer Nature, 2019, pp. 365-387. cited by applicant
Galitsky et al., Discovering Common Outcomes of Agents' Communicative Actions in Various Domains, Knowledge-Based Systems, vol. 24, No. 2, Mar. 2011, pp. 210-229. cited by applicant
Galitsky et al., Enabling a Bot with Understanding Argumentation and Providing Arguments., In Developing Enterprise Chatbots, Springer—Cham, Switzerland., 2019, pp. 465-532. cited by applicant
Galitsky et al., Explainable Machine Learning for Chatbots, In Developing Enterprise Chatbots, Springer, Cham, Switzerland, 2019, pp. 53-83. cited by applicant
Galitsky et al., Extending Tree Kernels Towards Paragraphs, International Journal of Computational Linguistics and Applications, vol. 5, No. 1, Jan.-Jun. 2014, pp. 105-116. cited by applicant
Galitsky et al., From Generalization of Syntactic Parse Trees to Conceptual Graphs, Proceedings of the 18th international conference on Conceptual structures: from information to intelligence, Jul. 2010, pp. 185-190. cited by applicant
Galitsky, Inductive Learning of Dispute Scenarios for Online Resolution of Customer Complaints, 3rd International IEEE Conference Intelligent Systems, Sep. 2006, pp. 103-108. cited by applicant
Galitsky et al., Interrupt Me Politely: Recommending Products and Services by Joining Human Conversation, Proceedings of the Workshop on Natural Language Processing in E-Commerce, Dec. 12, 2020, 11 pages. cited by applicant
Galitsky et al., Learning Adversarial Reasoning Patterns in Customer Complaints, Workshop at AAAI, (WS-11-06), 2011, 8 pages. cited by applicant
Galitsky, Learning Noisy Discourse Trees, Computational Linguistics and Intellectual Technologies, Proceedings of the International Conference Dialogue 2017. Available Online at: http://www.dialog-21.ru/media/3911/galitskyb.pdf, May 31-Jun. 3, 2017, 14 pages. cited by applicant
Galitsky, Matching Parse Thickets for Open Domain Question Answering, Data & Knowledge Engineering, vol. 107, Dec. 9, 2016, pp. 24-50. cited by applicant
Galitsky, Matching Parse Thickets for Open Domain Question Answering, Data & Knowledge Engineering, vol. 107, Jan. 2017, pp. 24-50. cited by applicant
Galitsky et al., Matching Sets of Parse Trees for Answering Multi-Sentence Questions, Proceedings of Recent Advances in Natural Language Processing, Sep. 2013, pp. 285-293. cited by applicant
Galitsky, Natural Language Understanding with the Generality Feedback, Discrete Mathematics and Theoretical Computer Science Technical Report 99-32, Jun. 1999, pp. 1-21. cited by applicant
Galitsky, On a Chatbot Conducting a Virtual Social Dialogue, 28th Association for Computing Machinery International Conference, Nov. 2019, 9 pages. cited by applicant
Galitsky et al., On a Chatbot Conducting Dialogue-in-Dialogue, Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, Sep. 11-13, 2019, pp. 118-121. cited by applicant
Galitsky et al., On a Chatbot Conducting Virtual Dialogues, CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, vol. 4, Nov. 3, 2019, pp. 2925-2928. cited by applicant
Galitsky et al., Programming Spatial Algorithms in Natural Language, Natural Language Processing, Available Online at: https://www.researchgate.net/publication/255598711_Programming_Spatial_Algorithms_in_Natural_Language, Jan. 2008, pp. 16-23. cited by applicant
Galitsky, Providing Personalized Recommendation for Attending Events Based on Individual Interest Profiles, Artificial Intelligence Research, vol. 5, No. 1, Sep. 2015, 37 pages. cited by applicant
Galitsky, Rhetorical Agreement: Maintaining Cohesive Conversations, Springer Nature, 2019, pp. 327-360. cited by applicant
Galitsky et al., Simulating the Conflict Between Reputation and Profitability for Online Rating Portals, Journal of Artificial Societies and Social Simulation vol. 8, No. 2, 2005, 21 pages. cited by applicant
Galitsky et al., Team Formation by Children with Autism, Research Gate, Available Online at: https://www.researchgate.net/publication/283023160, Jan. 2015, 10 pages. cited by applicant
Galitsky et al., Using Generalization of Syntactic Parse Trees for Taxonomy Capture on the Web, ICCS 2011, LNAI 6828, Jul. 2011, pp. 104-117. cited by applicant
Ganter et al., Pattern Structures and Their Projections, International Conference on Conceptual Structures, Jul. 30-Aug. 3, 2001, 16 pages. cited by applicant
Garcia-Villalba et al., A Framework to Extract Arguments in Opinion Texts, International Journal of Cognitive Informatics and Natural Intelligence, vol. 6, No. 3, pp. 62-87, Jul.-Sep. 2012. cited by applicant
Ghosh et al., Analyzing Argumentative Discourse Units in Online Interactions, Proceedings of the First Workshop on Argumentation Mining, Jun. 26, 2014, pp. 39-48. cited by applicant
Glickman et al., Web Based Probabilistic Textual Entailment, Computer Science Department, Available Online at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.125.6555&rep=rep1&type=pdf, Jan. 2005, 4 pages. cited by applicant
Golightly et al., Writing and Reading in the Disciplines, Pearson Custom Publishing, 2000. cited by applicant
Goutsos, Modeling Discourse Topic: Sequential Relations and Strategies in Expository Text, Text, vol. 16, No. 4, Dec. 1, 1996, pp. 501-533. cited by applicant
Grasso, Characterising Rhetorical Argumentation, Heriot-watt University, Department of Computing & Electrical Engineering, Jun. 2003, 235 pages. cited by applicant
Grasso, Playing with RST: Two Algorithms for the Automated Manipulation of Discourse Trees, Text, Speech and Dialogue: Second International Workshop, TSD'99 Plzen, Czech Republic, Proceedings, Conference Paper, Jan. 1999, pp. 357-360. cited by applicant
Greenberg, Conversational Experiences: Building Relationships One Conversation at a Time, Social CRM: The Conversation, Oct. 30, 2018, 10 pages. cited by applicant
Gronroos, The Relationship Marketing Process: Communication, Interaction, Dialogue, Value, Journal of Business & Industrial Marketing, vol. 19, Issue 2, Mar. 2004, pp. 99-113. cited by applicant
Grosz et al., Attention, Intention and the Structure of Discourse, Computational Linguistics, vol. 12, No. 3, 1986, pp. 175-204. cited by applicant
Grosz et al., Centering: A Framework for Modeling the Local Coherence of Discourse, Computational Linguistics, vol. 21, No. 2, Jun. 1995, 24 pages. cited by applicant
Grosz et al., Centering: A Framework for Modeling the Local Coherence of Discourse, Computational Linguistics, vol. 21, No. 2, Jan. 1995, pp. 203-225. cited by applicant
Grudin, The Rise of Incompetence, Association for Computing Machinery Interactions, Available Online at: https://interactions.acm.org/archive/view/january-february-2016/the-rise-of-incompetence, Jan.-Feb. 2016, 2 pages. cited by applicant
Gundel et al., Cognitive Status and The Form of Referring Expressions in Discourse, Language, vol. 69, No. 2, Jun. 1993, pp. 274-307. cited by applicant
Hai et al., Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Nov. 1-5, 2016, pp. 1817-1826. cited by applicant
Hall et al., The Weka Data Mining Software: An Update, Special Interest Group on Knowledge Discovery and Data Mining, Explorations Newsletter, vol. 11, No. 1, Available Online at: http://dx.doi.org/10.1145/1656274.1656278, Jun. 2009, pp. 10-18. cited by applicant
Halliday et al., Cohesion in English, vol. 14, No. 1, 1980, pp. 47-50. cited by applicant
Hasida et al., SemAF: Discourse Structures, Online Available at: http://slideplayer.com/slide/6408486/, 2018, 6 pages. cited by applicant
Heerschop et al., Polarity Analysis of Texts Using Discourse Structure, In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM '11, Oct. 2011, 10 pages. cited by applicant
Heilman et al., Good Question! Statistical Ranking for Question Generation, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Jun. 2010, pp. 609-617. cited by applicant
Hobbs, Coherence and Coreference, Cognitive Science, vol. 3, No. 1, Jan.-Mar. 1979, pp. 67-90. cited by applicant
Hoffman, Financial Report Ontology, Available online at: http://www.xbrlsite.com/2015/fro/, 2015, 2 Pages. cited by applicant
Hogenboom et al., Polarity Classification Using Structure-Based Vector Representations of Text, Decision Support Systems, vol. 74, Mar. 12, 2015, 18 pages. cited by applicant
Hogenboom et al., Using Rhetorical Structure in Sentiment Analysis, Communications of the ACM, vol. 58, No. 7, Jul. 2015, pp. 69-77. cited by applicant
Ibeke et al., Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences, Proceedings of the Eighth International Joint Conference on Natural Language Processing, vol. 2, Nov. 27-Dec. 1, 2017, pp. 395-400. cited by applicant
Ilvovsky, Going Beyond Sentences When Applying Tree Kernels, Proceedings of the Student Research Workshop, vol. 20, No. 4, Jun. 22-27, 2014, pp. 56-63. cited by applicant
Indian Application No. 201947044096, First Examination Report mailed on Jan. 13, 2022, 5 pages. cited by applicant
Indian Application No. 202047007447, First Examination Report, Sep. 9, 2021, 6 pages. cited by applicant
Jansen et al., Worldtree: a Corpus of Explanation Graphs for Elementary Science Questions Supporting Multi-Hop Inference, CoRR, abs/1802.03052, 2018, 9 pages. cited by applicant
Jasinskaja et al., Rhetorical Relations, Aug. 13, 2015, pp. 1-23. cited by applicant
Ji et al., A Neural Discourse Structure for Text Categorization, ACL, Aug. 2017, 10 pages. cited by applicant
Ji et al., Neural Discourse Structure for Text Categorization, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Jul. 30-Aug. 4, 2017, pp. 996-1005. cited by applicant
Ji et al., Representation Learning for Text-Level Discourse Parsing, Association for Computational Linguistics, Available Online at: https://www.aclweb.org/anthology/P14-1002, Jun. 2014, pp. 13-24. cited by applicant
Jijkoun et al., Recognizing Textual Entailment Using Lexical Similarity, Available Online at: https://u.cs.biu.ac.il/˜nlp/RTE1/Proceedings/jijkoun_and_de_rijke.pdf, Jan. 2005, 4 pages. cited by applicant
Jindal et al., Opinion Spam and Analysis, Proceeding WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining, Feb. 11-12, 2008, pp. 219-229. cited by applicant
Joachims et al., Cutting-Plane Training of Structural SVMs, Machine Learning, vol. 77, No. 1, Oct. 2009, pp. 27-59. cited by applicant
Johnson et al., Procedural Generation of Linguistics, Dialects, Naming Conventions and Spoken Sentences, Proceedings of 1st International Joint Conference of DiGRA and FDG, 2016, 9 pages. cited by applicant
Jorgensen et al., Challenges of Studying and Processing Dialects in Social Media, Proceedings of the ACL 2015 Workshop on Noisy User-Generated Text, Jul. 31, 2015, pp. 9-18. cited by applicant
Kan et al., Linear Segmentation and Segment Significance, Department of Computer Science and Center for Research on Information Access, Sep. 15, 1998, 9 pages. cited by applicant
Kelley, An Iterative Design Methodology for User-Friendly Natural Language Office Information Applications, ACM Transaction on Information Systems, vol. 2, No. 1, Mar. 1984, pp. 26-41. cited by applicant
Kerly et al., Bringing Chatbots into Education: Towards Natural Language Negotiation of Open Learner Models, Knowledge-Based Systems, vol. 20, No. 2, Dec. 2006, 14 pages. cited by applicant
Khan et al., SWIMS: Semi-Supervised Subjective Feature Weighting and Intelligent Model Selection for Sentiment Analysis, Knowledge-Based Systems, vol. 100, May 15, 2016, pp. 97-111. cited by applicant
Khardon et al., The Subsumption Lattice and Query Learning, Journal of Computer and System Sciences, vol. 72, No. 1, Feb. 2006, pp. 72-94. cited by applicant
Kittredge et al., An Advanced English Grammar with Exercises, The Athenaeum Press, 1913, 266 pages. cited by applicant
Klenner, A Model for Multi-Perspective Opinion Inferences, Proceedings of IJCAI Workshop Natural Language Meets Journalism, Jul. 9, 2016, pp. 6-11. cited by applicant
Kocisky et al., The NarrativeQA Reading Comprehension Challenge, Transactions of the Association for Computational Linguistics, vol. 6, May 2018, pp. 317-328. cited by applicant
Koiti, Presentation on Theme: WG2 PWI24617-5 SemAF, Discourse Structure, Berlin HASIDA Koiti AIST, Accessed from Internet on Oct. 14, 2019, 2 pages. cited by applicant
Kong, Are Simple Business Request Letters Really Simple? A Comparison of Chinese and English Business Request Letters, Text-Interdisciplinary Journal for the Study of Discourse, vol. 18, No. 1, 1998, pp. 103-141. cited by applicant
Kostelnik et al., Chatbots For Enterprises: Outlook, Acta Universitatis Agriculturae ET Silviculturae Mendelianae Brunensis, vol. 67, No. 6, 2019, pp. 1541-1550. cited by applicant
Kovalerchuk et al., Toward Virtual Data Scientist with Visual Means, International Joint Conference on Neural Networks, Feb. 2017, pp. 3073-3080. cited by applicant
Krakovna et al., Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models, ICML Workshop on Human Interpretability in Machine Learning, Sep. 30, 2016, pp. 46-50. cited by applicant
Kuchaiev et al., Topological Network Alignment Uncovers Biological Function and Phylogeny, Journal of the Royal Society Interface, vol. 7, Mar. 24, 2010, pp. 1341-1354. cited by applicant
Kumar et al., A Framework for Automatic Question Generation from Text using Deep Reinforcement Learning, Proceedings of the 2019 IJCAI Workshop SCAI: The 4th International Workshop on Search-Oriented Conversational AI, Aug. 3, 2019, 7 pages. cited by applicant
Kuyten et al., A Discourse Search Engine based on Rhetorical Structure Theory, Advances in Information Retrieval: 37th European Conference on IR Research, Mar. 2015, 12 pages. cited by applicant
Kwiatkowski et al., Natural Questions: a Benchmark for Question Answering Research, Transactions of the Association of Computational Linguistics, 2019, 14 pages. cited by applicant
Kwiatkowski et al., Scaling Semantic Parsers With on-the-fly Ontology Matching, Empirical Methods in Natural Language Processing, Oct. 18-21, 2013, pp. 1545-1556. cited by applicant
Kwon et al., Identifying and Classifying Subjective Claims, The Proceedings of the 8th Annual International Digital Government Research Conference, May 20-23, 2007, pp. 76-81. cited by applicant
Lake et al., Human-Level Concept Learning Through Probabilistic Program Induction, Science, vol. 350, No. 6266, Dec. 11, 2015, pp. 1332-1338. cited by applicant
Lan et al., Albert: A Lite BERT for Self-Supervised Learning of Language Representations, In Conference Paper at ICLR 2020, Feb. 9, 2020, pp. 1-17. cited by applicant
Lawless et al., AI and the Mitigation of Error: a Thermodynamics of Teams, Association for the Advancement of Artificial Intelligence Spring Symposia, 2016, 8 pages. cited by applicant
Lawrence et al., Combining Argument Mining Techniques, Working Notes of the 2nd Argumentation Mining Workshop, Jun. 4, 2015, pp. 127-136. cited by applicant
Lawrence et al., Mining Argumentative Structure from Natural Language Text Using Automatically Generated Premise-Conclusion Topic Models, Proceedings of the 4th Workshop on Argument Mining, Sep. 8, 2017, pp. 39-48. cited by applicant
Lee, Genres, Registers, Text Types, Domain, and Styles: Clarifying the Concepts and Navigating a Path Through the BNC Jungle, Language Learning & Technology, vol. 5, No. 3, Sep. 2001, pp. 37-72. cited by applicant
Levchuk et al., Active Inference in Multiagent Systems: Context-driven Collaboration and Decentralized Purpose-driven Team Adaptation, Artificial Intelligence for the Internet of Everything, 2019, 9 pages. cited by applicant
Li et al., Adversarial Learning for Neural Dialogue Generation, Available Online at: https://www.aclweb.org/anthology/D17-1230.pdf, Sep. 2017, 13 pages. cited by applicant
Li et al., Annotating the Little Prince with Chinese AMRs, LAW-2016, Berlin, Germany, Aug. 11, 2016, 9 pages. cited by applicant
Li et al., Chinese Abstract Meaning Representation, Available online at: https://catalog.Idc.upenn.edu/LDC2021T13, 2019, 2 pages. cited by applicant
Li et al., DailyDialog: a Manually Labelled Multi-Turn Dialogue Dataset, Proceedings of the Eighth International Joint Conference on Natural Language Processing, Long Papers, vol. 1, Dec. 1, 2017, pp. 986-995. cited by applicant
Li et al., Deep Reinforcement Learning for Dialogue Generation, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Sep. 2016, pp. 1192-1202. cited by applicant
Liang, Lambda Dependency-based Compositional Semantics, Technical report, arXiv., Sep. 19, 2013, 7 pages. cited by applicant
Lin et al., A PDTB-Styled End-to-End Discourse Parser, Natural Language Engineering, vol. 20, No. 2, Apr. 2014, pp. 151-184. cited by applicant
Ling et al., Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems, CoRR, abs/1705.04146, Oct. 23, 2017, 10 pages. cited by applicant
Lioma et al., Rhetorical Relations for Information Retrieval, SIGIR, Apr. 2012, pp. 931-940. cited by applicant
Lippi et al., Argument Mining from Speech: Detecting Claims in Political Debates, AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Feb. 2016, pp. 2979-2985. cited by applicant
Lippi et al., Argumentation Mining: State of the Art and Emerging Trends, ACM Transactions on Internet Technology, vol. 16, No. 2, Article 10, Mar. 2016, pp. 1-25. cited by applicant
Lippi et al., Margot: a Web Server for Argumentation Mining, Expert Systems with Applications, vol. 65, Dec. 2016, pp. 292-303. cited by applicant
Liu et al., How Not to Evaluate Your Dialogue System: an Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation, Proceedings of the Conference on Empirical Methods in Natural Language Processing, Jan. 3, 2017, 15 pages. cited by applicant
Liu et al., Roberta: a Robustly Optimized Bert Pretraining Approach, arXiv:1907.11692, Available Online at: https://arxiv.org/abs/1907.11692, Jul. 26, 2019, 13 pages. cited by applicant
Liu et al., Towards Better Analysis of Deep Convolutional Neural Networks, IEEE Transactions on Visualization and Computer Graphics, vol. 23, No. 1, May 2016, pp. 1-10. cited by applicant
Lo Cascio et al., Grammatica dell'Argomentare: strategie e strutture, [A grammar of Arguing: strategiesand structures] Firenze: La Nuova Italia, 1991, pp. 662-663. cited by applicant
Logacheva et al., ConvAI Dataset of Topic-Oriented Human-to-Chatbot Dialogues, The NIPS '17 Competition: Building Intelligent Systems, 2018, pp. 47-57. cited by applicant
Louis et al., Discourse Indicators for Content Selection in Summaization, SIGDIAL Conference, The Association for Computer Linguistics, Sep. 2010, pp. 147-156. cited by applicant
Lowe et al., On the Evaluation of Dialogue Systems with Next Utterance Classification, In Special Interest Group on Discourse and Dia-logue, Jul. 2016, pp. 264-269. cited by applicant
Luan et al., LSTM based Conversation Models, Department of Electrical Engineering, University of Washington, Mar. 31, 2016, 5 pages. cited by applicant
Lucas, Computer-Assisted Text Analysis for Comparative Politics, Political Analysis, vol. 23 , No. 2, Feb. 4, 2015, pp. 254-277. cited by applicant
Lyons et al., Trust and Human-Machine Teaming: a Qualitative Study, Artificial Intelligence for the Internet of Everything, 2019, pp. 101-116. cited by applicant
Macagno, Argumentation Schemes, Cambridge University Press, 2008, pp. 517-573. cited by applicant
Macewan, The Essentials of Argumentation, D. C. Heath, 1898, 474 pages. cited by applicant
Makhalova et al., Information Retrieval Chatbots Based on Conceptual Models, In book: Graph-Based Representation and Reasoning, Jun. 2019, pp. 230-238. cited by applicant
Makhalova et al., Pattern Structures for News Clustering, Proceedings of the 4th International Conference on What can FCA do for Artificial Intelligence, vol. 1430, Jul. 2015, pp. 35-42. cited by applicant
Mann et al., Rhetorical Structure Theory: Description and Construction of Text Structures, University of Southern California, Oct. 1986, 22 pages. cited by applicant
Marcu, From Discourse Structures to Text Summaries, Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, 1997, pp. 82-88. cited by applicant
Marcu, The Theory and Practice of Discourse Parsing and Summarization, MIT Press, Nov. 2000, 248 pages. cited by applicant
Marir et al., Rhetorical Structure Theory for Content-Based Indexing and Retrieval of Web Documents, ITRE 2004, 2nd International Conference Information Technology: Research and Education, Jun. 2004, pp. 160-164. cited by applicant
Markle-Huss et al., Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures, 50th Hawaii International Conference on System Sciences, Jan. 2017, pp. 1142-1151. cited by applicant
Marks et al., Want Classified Information? Talk to The Chatbot, New Scientist, vol. 223, No. 2980, Aug. 2, 2014, p. 22. cited by applicant
Matousek et al., Text, Speech and Dialogue, Second International Workshop, TSD'99 Plzen, Czech Republic, Sep. 13-17, 1999, 11 pages. cited by applicant
May et al., SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation, Proceedings of the 11th International Workshop on Semantic Evaluation, Aug. 3-4, 2017, pp. 536-545. cited by applicant
Maziero et al., Revisiting Cross-Document Structure Theory for Multi-Document Discourse Parsing, Information Processing & Management, vol. 50, Issue 2, Mar. 2014, pp. 297-314. cited by applicant
Mcnamara et al., Are Good Texts Always Better? Interactions of Text Coherence, Background Knowledge, and Levels of Understanding in Learning from Text, Cognition and Instruction, vol. 14, No. 1, Mar. 1996, pp. 1-43. cited by applicant
Mercier et al., Why Do Humans Reason? Arguments for an Argumentative Theory, Behavioral and Brain Sciences, vol. 34, No. 2, Apr. 2011, pp. 57-111. cited by applicant
Miceli et al., Emotional and Non-Emotional Persuasion, Applied Artificial Intelligence, Jun. 2006, pp. 1-25. cited by applicant
Mikolov et al., Efficient Estimation of Word Representations in Vector Space, Available Online at: https://arxiv.org/pdf/1301.3781.pdf, Sep. 7, 2013, pp. 1-12. cited by applicant
Milenkovic et al., Uncovering Biological Network Function via Graphlet Degree Signatures, Cancer Informatics, vol. 6, Apr. 14, 2008, pp. 257-273. cited by applicant
Mitkov et al., A Computer-Aided Environment for Generating Multiple-Choice test items, Natural Language Engineering, vol. 12, No. 2, Jun. 2006, pp. 177-194. cited by applicant
Mittu et al., Introduction to the Symposium on AI and the Mitigation of Human Error, Association for the Advancement of Artificial Intelligence Spring Symposia, 2016, 4 pages. cited by applicant
Moens et al., Automatic Detection of Arguments in Legal Texts, Proceedings of the 11th International Conference on Artificial Intelligence and Law, ICAIL 2007, Jun. 4-8, 2007, pp. 225-230. cited by applicant
Molnar, Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, Available online at: https://christophm.github.io/interpretable-ml-book/, Aug. 14, 2018, 185 pages. cited by applicant
Morato et al., Experiments in Discourse Analysis Impact on Information Classification and Retrieval Algorithms, Inf. Process. Manage. vol. 39, 2003, pp. 825-851. cited by applicant
Moschitti, Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees, Proceedings of the 17th European Conference on Machine Learning, Sep. 18-22, 2006, pp. 318-329. cited by applicant
Moskowitz et al., Human Caused Bifurcations in a Hybrid Team—A Position Paper, Association for the Advancement of Artificial Intelligence Spring Symposia, 2016, 4 pages. cited by applicant
Mukherjee et al., Fake Review Detection: Classification and Analysis of Real and Pseudo Reviews, Technical Report, Department of Computer Science, 2013, 11 pages. cited by applicant
Mukherjee et al., What Yelp Fake Review Filter Might Be Doing?, Proceedings of the Seventh International Association for the Advancement of Artificial Intelligence Conference on Weblogs and Social Media, Jan. 2013, pp. 409-418. cited by applicant
Murphy et al., What Makes a Text Persuasive? Comparing Students' and Experts' Conceptions of Persuasiveness, International Journal of Educational Research, vol. 35, pp. 675-698, 2001. cited by applicant
Nakamura et al., Another Diversity-Promoting Objective Function for Neural Dialogue Generation, Association for the Advancement of Artificial Intelligence, Nov. 21, 2018, 8 pages. cited by applicant
Narasimhan, Nvidia Clocks Worlds Fastest Bert Training Time and Largest Transformer Based Model, Paving Path for Advanced Conversational AI, nVIDIA Developer Blog, Available Online at: https://developer.nvidia.com/blog/training-bert-with-gpus/, Aug. 13, 2019, 5 pages. cited by applicant
Narducci et al., Improving the User Experience with a Conversational Recommender System, International Conference of the Italian Association for Artificial Intelligence, 2018, pp. 528-538. cited by applicant
Nguyen et al., A Neural Local Coherence Model, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Jul. 2017, pp. 1320-1330. cited by applicant
Novikova et al., Why We Need New Evaluation Metrics for NLG, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Sep. 7-11, 2017, pp. 2241-2252. cited by applicant
Oatley et al., Understanding Emotions, Wiley & Co., Feb. 1996. cited by applicant
Oppong, What Was I Thinking? (The Science of Systematic Irrationality), Available Online at: https://medium.com/kaizen-habits/what-was-i-thinking-the-science-of-systematic-irrationality-e053e5476fcf, Oct. 25, 2018, 6 pages. cited by applicant
Oraby et al., And That's a Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue, Proceedings of the 2nd Workshop on Argumentation Mining, Jun. 4, 2015, pp. 116-126. cited by applicant
O'Reilly et al., Reversing the Reverse Cohesion Effect: Good Texts Can Be Better for Strategic, High-Knowledge Readers, Discourse Processes, vol. 43, No. 2, Dec. 2007, pp. 121-152. cited by applicant
Ospina et al., Eliciting the Patient's Agenda—Secondary Analysis of Recorded Clinical Encounters, Journal of General Internal Medicine, vol. 34, No. 1, 2018, pp. 36-40. cited by applicant
Ouyang et al., Improving Recommendation by Deep Latent Factor-Based Explanation, Associated for the Advancement of Artificial Intelligence, 2020, 9 pages. cited by applicant
Palmer, SemLink: Linking PropBank, VerbNet and FrameNet, Proceedings of the Generative Lexicon Conference, GL 2009, Sep. 17, 2009, 54 pages. cited by applicant
Papineni et al., Bleu: A Method for Automatic Evaluation of Machine Translation, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Jul. 2002, pp. 311-318. cited by applicant
Park et al., Multimodal Explanations: Justifying Decisions and Pointing to the Evidence, CoRR, abs/1802.08129, Feb. 15, 2018, 11 pages. cited by applicant
International Application No. PCT/US2018/031890, International Search Report and Written Opinion mailed on Aug. 17, 2018, 12 pages. cited by applicant
International Application No. PCT/US2018/053376, International Preliminary Report on Patentability mailed on Apr. 9, 2020, 12 pages. cited by applicant
International Application No. PCT/US2018/053376, International Search Report and Written Opinion mailed on Feb. 11, 2019, 18 pages. cited by applicant
International Application No. PCT/US2018/053376, Invitation to Pay Add'l Fees and Partial Search Report mailed on Dec. 21, 2018, 11 pages. cited by applicant
International Application No. PCT/US2018/053392, International Search Report and Written Opinion mailed on Dec. 17, 2018, 11 pages. cited by applicant
Pelsmaekers et al., Rhetorical Relations and Subordination in L2 Writing, Linguistic Choice Across Genres: Variation in Spoken and Written English, Jul. 15, 1998, pp. 191-213. cited by applicant
Pennington et al., GloVe: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 25-29, 2014, pp. 1532-1543. cited by applicant
Persing et al., Modeling Argument Strength in Student Essays, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Jul. 26-31, 2015, pp. 543-552. cited by applicant
Peter et al., The Peter Principle: Why Things Always Go Wrong, William Morrow and Company, 1968. cited by applicant
Peters et al., Deep Contextualized Word Representations, Allen Institute for Artificial Intelligence, Paul G. Allen School of Computer Science & Engineering, University of Washington, Mar. 22, 2018, 15 pages. cited by applicant
Peters et al., Deep Contextualized Word Representations, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, Jun. 1-6, 2018, pp. 2227-2237. cited by applicant
Pisarevskaya et al., An Anatomy of a Lie: Discourse Patterns in Customer Complaints Deception Dataset, In Companion Proceedings of The 2019 World Wide Web Conference, 2019, pp. 373-380. cited by applicant
Piwek et al., T2D: Generating Dialogues Between Virtual Agents Automatically from Text, Intelligent Virtual Agents, 2007, pp. 161-174. cited by applicant
Plotkin, A Note on Inductive Generalization, Machine Intelligence 5, Chapter 8, 1970, pp. 153-163. cited by applicant
Poesio et al., Centering: A Parametric Theory and Its Instantiations, Computational Linguistics, vol. 30, No. 3, Sep. 2004, pp. 309-363. cited by applicant
Polleti et al., Explanations Within Conversational Recommendation Systems: Improving Coverage Through Knowledge Graph Embeddings, Association for the Advancement of Artificial Intelligence (www.aaai.org), 2020, 8 pages. cited by applicant
Prasad et al., The Penn Discourse TreeBank 2.0, Proceedings of the Sixth International Conference on Language Resources and Evaluation, 2008, 8 pages. cited by applicant
Radev, A Common Theory of Information Fusion from Multiple Text Sources Step One: Cross-document Structure, Proceedings of the 1st SIGDial Workshop on Discourse and Dialogue, vol. 10, Oct. 7-8, 2000, pp. 74-83. cited by applicant
Rajpurkar et al., Know What You Don't Know: Unanswerable Questions for SQuAD, arXiv:1806.03822, Available Online at: https://arxiv.org/pdf/1806.03822.pdf, Jun. 11, 2018, 9 pages. cited by applicant
Rajpurkar et al., SQuAD: 100,000+ Questions for Machine Comprehension of Text, Proceedings of the Conference on Empirical Methods in Natural Language Processing, Available online at: https://doi.org/10.18653/v1/D16-1264, Oct. 11, 2016, pp. 2383-2392. cited by applicant
Rayana et al., Collective Opinion Spam Detection: Bridging Review Networks and Metadata, Proceedings of the 21st Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery and Data Mining, Aug. 11-14, 2015, 10 pages. cited by applicant
Redeker, Coherence and Structure in Text and Discourse, In: William Black & Harry Bunt (eds.), Abduction, Belief and Context in Dialogue. Studies in Computational Pragmatics, Jan. 2000, pp. 1-28. cited by applicant
Ribeiro et al., Why Should I Trust You? Explaining the Predictions of Any Classifier, KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Available Online at: https://dl.acm.org/doi/10.1145/2939672.2939778, Aug. 2016, pp. 1135-1144. cited by applicant
Ritter et al., Data-Driven Response Generation in Social Media, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Jul. 27-31, 2011, pp. 583-593. cited by applicant
Robinson, A Machine-Oriented Logic Based on the Resolution Principle, Journal of the Association for Computing Machinery, vol. 12, No. 1, Jan. 1965, pp. 23-41. cited by applicant
Rose et al., Discourse Processing of Dialogues with Multiple Threads, In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, Jun. 26-30, 1995, pp. 31-38. cited by applicant
Ruder, An Overview of Gradient Descent Optimization Algorithms, Available Online at: https://arxiv.org/pdf/1609.04747.pdf, Jun. 15, 2017, 14 pages. cited by applicant
Ruppenhofer et al., FrameNet II: Extended Theory and Practice, Available Online at: https://framenet.icsi.berkeley.edu/fndrupal/the_book., 2016, 129 pages. cited by applicant
Sadek et al., A Discourse-Based Approach for Arabic Question Answering, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 16, No. 2, Article 11, Nov. 2016, pp. 1-18. cited by applicant
Sadek et al., Arabic Rhetorical Relations Extraction for Answering Why and How to Questions, NLDB'12: Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems, Jun. 2012, pp. 385-390. cited by applicant
Sakai, Alternatives to Bpref, Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2007, pp. 71-78. cited by applicant
Salton et al., On the Specification of Term Values in Automatic Indexing, Journal of Documentation, vol. 29, No. 4, 1973, 35 pages. cited by applicant
Sardianos et al., Argument Extraction from News, Proceedings of the 2nd Workshop on Argumentation Mining, Jun. 4, 2015, pp. 56-66. cited by applicant
Scheffler et al., Mapping PDTB-Style Connective Annotation to RST-Style Discourse Annotation, Proceedings of the 13th Conference on Natural Language Processing, 2016, pp. 242-247. cited by applicant
Schlosser, Can Including Pros and Cons Increase the Helpfulness and Persuasiveness of Online Reviews? The Interactive Effects of Ratings and Arguments, Journal of Consumer Psychology, vol. 21, No. 3, Jul. 2011, pp. 226-239. cited by applicant
Schnabel et al., Evaluation Methods for Unsupervised Word Embeddings, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Available Online at: https://www.aclweb.org/anthology/D15-1036.pdf, Sep. 17-21, 2015, pp. 298-307. cited by applicant
Schnedecker, Les Chaines De Reference Dans Les Portraits Journalistiques : Elements De Description, Travaux de Linguistique, Jan. 2005, pp. 85-133. cited by applicant
Schnedecker, Nom Propre Et Chaines De Reference, Recherches Linguistiques, 1997, 231 pages. cited by applicant
Scholman et al., Examples and Specifications That Prove a Point: Identifying Elaborative and Argumentative Discourse Relations, Dialogue & Discourse, vol. 8, No. 2, Jul. 2017, pp. 56-83. cited by applicant
Schulz et al., A Frame Tracking Model for Memory-Enhanced Dialogue Systems, Available Online at: https://arxiv.org/pdf/1706.01690.pdf, Jun. 6, 2017, 9 pages. cited by applicant
Selivanov, GloVe Word Embeddings, Available Online at: https://cran.rproject.org/web/packages/text2vec/vignettes/glove.html, Feb. 18, 2020, 4 pages. cited by applicant
Seo et al., Online Community Search Using Thread Structure, CIKM '09 Proceedings of the 18th ACM conference on Information and knowledge Management, 2009, 4 pages. cited by applicant
Seo et al., Online Community Search Using Thread Structure, In Proceedings of the 18th ACM Conference on Information and Knowledge Management, Nov. 2009, 4 pages. cited by applicant
Serasset et al., Association for Computational Linguistics, Stroudsburg, pp. 7-14. cited by applicant
Serban et al., A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues, Proceedings of the Thirty-First Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, Jun. 14, 2016, 15 pages. cited by applicant
Serban et al., A Survey of Available Corpora for Building Data-Driven Dialogue Systems, Available online at: https://arxiv.org/pdf/1512.05742.pdf, Mar. 21, 2017, 56 pages. cited by applicant
Serban et al., Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models, Proceedings of the Thirtieth Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, Apr. 6, 2016, 8 pages. cited by applicant
Serban et al., Generating Factoid Questions with Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1, May 29, 2016, 13 pages. cited by applicant
Severyn et al., Fast Support Vector Machines for Convolution Tree Kernels, Data Mining Knowledge Discovery, vol. 25, No. 2, Sep. 2012, 33 pages. cited by applicant
Shaw, 3 Reasons Why People Say One Thing and Do Another, Available online at: https://beyondphilosophy.com/3-reasons-why-people-say-one-thing-and-do-another/, May 22, 2015, 7 pages. cited by applicant
Shuldiner, Raising Them Right: AI and the Internet of Big Things, Chapter 8, Artificial Intelligence for the Internet of Everything, 2019, pp. 139-143. cited by applicant
Sidorov et al., Syntactic N-Grams as Machine Learning Features for Natural Language Processing, Expert Systems with Applications, vol. 41, Issue 3, Feb. 15, 2014, pp. 853-860. cited by applicant
Sjoera, The Linguistics Behind Chat Bots, iCapps, Available Online at: http://www.icapps.com/the-linguistics-behind-chatbots/, Feb. 22, 2017, 9 pages. cited by applicant
Socher et al., Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Oct. 2013, pp. 1631-1642. cited by applicant
Somasundaran et al., Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, In EMNLP, ACL, Aug. 2009, pp. 170-179. cited by applicant
Soricut et al., Sentence Level Discourse Parsing using Syntactic and Lexical Information, Proceedings of HLT-NAACL, Available Online at: https://www.aclweb.org/anthology/J02-4002, May-Jun. 2003, pp. 149-156. cited by applicant
Stab et al., Identifying Argumentative Discourse Structures in Persuasive Essays, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 2014, pp. 46-56. cited by applicant
Stab et al., Recognizing Insufficiently Supported Arguments in Argumentative Essays, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), vol. 1, Apr. 3-7, 2017, pp. 980-990. cited by applicant
Stab et al., Recognizing the Absence of Opposing Arguments in Persuasive Essays, Proceedings of the 3rd Workshop on Argument Mining, Aug. 7-12, 2016, pp. 113-118. cited by applicant
Suchanek et al., A Core of Semantic Knowledge, In Proceedings of the 16th international conference on World Wide Web, May 8-12, 2007, pp. 697-706. cited by applicant
Sun et al., Conversational Recommender System, Available Online at https://arxiv.org/pdf/1806.03277.pdf, Jun. 8, 2018, 10 pages. cited by applicant
Sun et al., Discourse Processing for Context Question Answering Based on Linguistic Knowledge, Knowledge-Based System, vol. 20, Aug. 2007, pp. 1-23. cited by applicant
Sun et al., Exploiting Product Related Review Features for Fake Review Detection, Mathematical Problems in Engineering, vol. 2016, Article ID 4935792, Jul. 4, 2016, 8 pages. cited by applicant
Suwandaratna et al., Discourse Marker based Topic Identification and Search Results Refining, 5th International Conference on In Information and Automation for Sustainability (ICIAFs), 2010, pp. 119-125. cited by applicant
Suwandaratna et al., Discourse Marker Based Topic Identification and Search Results Refining, In Information and Automation for Sustainability (ICIAFs), 5th International Conference, 2010, pp. 119-125. cited by applicant
Taboada et al., Rhetorical Structure Theory: Looking Back and Moving Ahead, Discourse Studies, vol. 8, No. 3, Jan. 24, 2006, pp. 423-459. cited by applicant
Taboada, The Genre Structure of Bulletin Board Messages, Text Technology, vol. 13, No. 2, Nov. 2, 2004, pp. 55-82. cited by applicant
Teufel et al., Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status, Computational Linguistics, vol. 28, No. 4, Dec. 2002, pp. 409-445. cited by applicant
Theranos, Wall Street Journal: Letter to the Editor, Theranos Works to Realize Access to Preventive Care, Available Online at: https://theranos.com/news/posts/wall-street-journal-letter-to-the-editor, Dec. 22, 2015, 4 pages. cited by applicant
Thompson et al., A Personalized System for Conversational Recommendations, Journal of Artificial Intelligence Research, vol. 21, No. 1, Mar. 2004, pp. 393-428. cited by applicant
Todirascu et al., Coherence and Cohesion for the Assessment of Text Readability, Proceedings of NLPCS 2013, Oct. 2013, pp. 11-19. cited by applicant
Torrance et al., Rhetorical Structure Analysis as a Method for Understanding Writing Processes, Presented at the Multidisciplinary Approaches to Discourse (MAD) Workshop, Information Technology Research Institute Technical Report Series, Aug. 2001, 9 pages. cited by applicant
Toulmin, The Uses of Argument, Cambridge At the University Press, 1958, 259 pages. cited by applicant
Traum, Rhetorical Relations, Action and Intentionality in Conversation, Proceedings ACL SIG Workshop on Intentionality and Structure in Discourse Relations, Jun. 1993, pp. 132-135. cited by applicant
Trstenjak et al., KNN with TF-IDF Based Framework for Text Categorization, Procedia Engineering, vol. 69, 24th Danube Adria Association for Automation and Manufacturing International Symposium on Intelligent Manufacturing and Automation, 2014, pp. 1356-1364. cited by applicant
Tseng et al., Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation, Available Online at: https://arxiv.org/pdf/1910.06719.pdf, Oct. 2, 2019, 10 pages. cited by applicant
Turek, Explainable Artificial Intelligence (XAI), Available Online at: https://www.darpa.mil/program/explainable-artificial-intelligence, Accessed from Internet on Aug. 27, 2019, pp. 1-10. cited by applicant
Turner, A Model Explanation System: Latest Updates and Extensions, ICML Workshop on Human Interpretability in Machine Learning, Available Online at: https://arxiv.org/pdf/1606.09517v1.pdf, Jun. 30, 2016, pp. 1-5. cited by applicant
Van Der Wees et al., Five Shades of Noise: Analyzing Machine Translation Errors in User-Generated Text, Proceedings of the ACL 2015 Workshop on Noisy User-generated Text, Jul. 31, 2015, pp. 28-37. cited by applicant
Van Dijk, Explorations in the Semantics and Pragmatics of Discourse, Text and Context, Longman Linguistics Library, 1977, 274 pages. cited by applicant
Van Eemeren et al., Fundamentals of Argumentation Theory: A Handbook of Historical Backgrounds and Contemporary Developments, Routledge, Taylor & Francis Group, Mar. 1, 1996, 440 pages. cited by applicant
Venkatesh et al., On Evaluating and Comparing Conversational Agents, 31st Conference on Neural Information Processing Systems, Jan. 2018, 10 pages. cited by applicant
Verberne et al., Discourse-Based Answering of Why-Questions, TAL Traitement Automatique des Langues, vol. 47, Sep. 2007, pp. 21-41. cited by applicant
Virtanen, Analysing Argumentative Strategies: A Reply to a Complaint, Anglicana Turkuensia, vol. 14, 1995, pp. 539-547. cited by applicant
Vorontsov et al., Additive Regularization of Topic Models, Machine Learning, vol. 101, 2015, 21 pages. cited by applicant
Walia et al., Semantic Features for Automated Answer Scoring, International Journal if Advance Research in Science and Engineering, vol. 6, No. 10, Oct. 2017, 6 pages. cited by applicant
Walton et al., Argumentation Schemes, Cambridge University Press, Aug. 2008, 456 pages. cited by applicant
Walton, Argumentation Schemes for Presumptive Reasoning, Lawrence Erlbaum Associates, Publishers, 1996, 39 pages. cited by applicant
Walton, Dialogical Models of Explanation, Explanation-Aware Computing: Papers from the Association for the Advancement of Artificial Intelligence Workshop, Technical Report WS-07-06, Association for the Advancement of Artificial Intelligence Workshop Press, Jan. 2007, 9 pages. cited by applicant
Wang et al., A Joint Model for Question Answering and Question Generation, Learning to Generate Natural Language Workshop, ICML, Jun. 5, 2017, 7 pages. cited by applicant
Wang et al., A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the Believability of Observed Information, Proceedings of the SIGDIAL 2013 Conference, Aug. 2013, pp. 423-432. cited by applicant
Wang et al., An Information Retrieval Approach Based on Discourse Type, Proceedings of the 11th International Conference on Applications of Natural Language to Information Systems, 2006, pp. 197-202. cited by applicant
Wang et al., Predicting Thread Discourse Structure Over Technical Web Forums, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Jul. 2011, pp. 13-25. cited by applicant
Wang et al., Using Learning Analytics to Understand the Design of an Intelligent Language Tutor-Chatbot Lucy, International Journal of Advanced Computer Science and Applications, vol. 4, No. 11, Nov. 2013, pp. 124-131. cited by applicant
Webber et al., Discourse Structure and Language Technology, Natural Language Engineering, vol. 18, No. 4, Oct. 2012, pp. 437-490. cited by applicant
Wilks, Machine Conversations, The Springer International Series in Engineering and Computer Science, vol. 511, 1999, 38 pages. cited by applicant
Wolf et al., Representing Discourse Coherence: A Corpus-Based Study, Computational Linguistics, vol. 31, No. 2, Jun. 2005, pp. 134-140. cited by applicant
Yang et al., Reflections on Rational Choice-The Existence of Systematic Irrationality, The Journal of Socio-Economics. vol. 37, No. 3, Jun. 2008, pp. 1218-1233. cited by applicant
Yao et al., Information Extraction over Structured data: Question Answering with Freebase, In Proceedings of the 52nd ACL, Jun. 23-25, 2014, pp. 956-966. cited by applicant
Yao et al., Online Deception Detection Refueled by Real World Data Collection, Proceedings of Recent Advances in Natural Language Processing, Jul. 28, 2017, 10 pages. cited by applicant
Yih et al., Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base, Microsoft Research, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, Jul. 26-31, 2015, pp. 1321-1331. cited by applicant
Young et al., POMDP-Based Statistical Spoken Dialogue Systems: A Review, Proceedings of the IEEE, vol. 101, No. 5, May 2013, pp. 1160-1179. cited by applicant
Yu et al., Detecting User Engagement in Everyday Conversations, Available online at: https://arxiv.org/pdf/cs/0410027.pdf, Oct. 13, 2004, 4 pages. cited by applicant
Yuan et al., Machine Comprehension by Text-to-Text Neural Question Generation, Proceedings of the 2nd Workshop on Representation Learning for Natural Language Processing, Aug. 3, 2017, pp. 15-25. cited by applicant
Zhao et al., Application-Driven Statistical Paraphrase Generation, Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, Aug. 2-7, 2009, pp. 834-842. cited by applicant
Zhao et al., Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank, Oregon State University, Available online at: https://arxiv.org/pdf/1708.08484.pdf, Aug. 28, 2017, pp. 2117-2123. cited by applicant
Zhu et al., Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation, IEEE Conference on Computational Intelligence and Games (CIG), 2018, 8 pages. cited by applicant
U.S. Appl. No. 17/235,329, Notice of Allowance mailed on Aug. 9, 2023, 5 pages. cited by applicant
U.S. Appl. No. 17/235,329, Supplemental Notice of Allowability mailed on Aug. 30, 2023, 2 pages. cited by applicant
U.S. Appl. No. 17/652,366, Notice of Allowance mailed on Aug. 9, 2023, 5 pages. cited by applicant
U.S. Appl. No. 17/690,897, Corrected Notice of Allowability mailed on Aug. 4, 2023, 4 pages. cited by applicant
U.S. Appl. No. 17/690,897, Supplemental Notice of Allowance mailed on Sep. 8, 2023, 4 pages. cited by applicant
U.S. Appl. No. 18/151,164, Non-Final Office Action mailed on Oct. 26, 2023, 23 pages. cited by applicant
2009 Annual Study: Global Cost of a Data Breach, Ponemon Institute, LLC, PGP, Apr. 2010, 36 pages. cited by applicant
Data Loss Prevention, Trend Micro, Available online at http://www.trendmicro.co.in/cloud-content/us/pdfs/business/datasheets/ds_data-loss-prevention.pdf, 2010, 2 pages. cited by applicant
Data Loss Prevention Products & Services, Symantec, Available online at https:/www.symantec.com/products/data-loss-prevention. Retrieved Aug. 30, 2018. cited by applicant
Global Security Report 2010, Trustwave, Available online at https://www.trustwave.com/Resources/Library/Documents/2010-Trustwave-Global-Security-Report/, 2010, 49 pages. cited by applicant
Malaysia Airlines Flight 17, Wikipedia, 2016, 38 pages. cited by applicant
Shadow Chairman of Investigative Committee, CrimeRussia, Available online at https://crimerussia.com/corruption/tenevoy-direktor-skr/, Aug. 2016, 5 pages. cited by applicant
Airenti et al., Conversational and Behavior Games in the Pragmatics of Discourse, Cognitive Science, vol. 17, Issue 2, Apr.-Jun. 1993, pp. 197-256. cited by applicant
Allen et al., Analyzing Intention in Utterances, Artificial Intelligence, vol. 15, No. 3, Dec. 1980, pp. 143-178. cited by applicant
Bengio et al., A Neural Probabilistic Language Model, Journal of Machine Learning Research, vol. 3, Feb. 3, 2003, pp. 1137-1155. cited by applicant
Blaylock, Managing Communicative Intentions in Dialogue Using a Collaborative Problem-Solving Model, The University of Rochester, Computer Science Department, Apr. 2002, 56 pages. cited by applicant
Blaylock et al., Managing Communicative Intentions with Collaborative Problem Solving, In Current and New Directions in Discourse and Dialogue, 2003, pp. 63-84. cited by applicant
Boni, Using Logical Relevance for Question Answering, Journal of Applied Logic, vol. 5, No. 1, Mar. 2007, pp. 92-103. cited by applicant
Boyer et al., MJRTY—A Fast Majority Vote Algorithm, Automated Reasoning, 1991, pp. 105-117. cited by applicant
Carlson et al., Discourse Tagging Reference Manual, Available Online at https://www.isi.edu/˜marcu/discourse/tagging-ref-manual.pdf, Sep. 2001, 87 pages. cited by applicant
Cohen, Enron Email Dataset, Available online at: https://www.cs.cmu.edu/˜enron/, Jul. 10, 2016, 1 page. cited by applicant
Cohen et al., Intention is Choice with Commitment, Artificial Intelligence, 1990, pp. 213-261. cited by applicant
Cristea et al., Veins Theory: A model of Global Discourse Cohesion and Coherence, In C. Boitet & P. Whitelock (Eds.), 17th International Conference on Computational linguistics, 1998, pp. 281-285. cited by applicant
Croft et al., Search Engines—Information Retrieval in Practice, Pearson Education, 2009, 542 pages. cited by applicant
Endres-Niggemeyer et al., Summarizing Text for Intelligent Communication, Dagstuhl Seminar Report 79, 30.12-17.12.93 (9350), 1995, 36 pages. cited by applicant
Dijkstra, Programming Considered as a Human Activity, Proc. IFIP Congress, 1965, 7 pages. cited by applicant
Feng et al., A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing, In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Jun. 23-25, 2014, pp. 511-521. cited by applicant
Feng, RST-Style Discourse Parsing and Its Applications in Discourse Analysis, University of Toronto, 2015, 189 pages. cited by applicant
Ferraiolo et al., Role-Based Access Controls, Proceedings of the 15th NIST-NSA National Computer Security Conference, Oct. 13-16, 1992, 11 pages. cited by applicant
Galitsky et al., Improving Text Retrieval Efficiency with Pattern Structures on Parse Thickets, Formal Concept Analysis Meets Information Retrieval, ECIR, 2013, 16 pages. cited by applicant
Galitsky et al., Improving Trust in Automation of Social Promotion, Association for the Advancement of Artificial Intelligence, 2014, pp. 28-35. cited by applicant
Galitsky et al., Inferring the Semantic Properties of Sentences by Mining Syntactic Parse Trees, Data & Knowledge Engineering, vols. 81-82, Nov.-Dec. 2012, 44 pages. cited by applicant
Galitsky et al., Learning Communicative Actions of Conflicting Human Agents, Journal of Experimental & Theoretical Artificial Intelligence, vol. 20, No. 4, Dec. 2008, pp. 277-317. cited by applicant
Galitsky, Learning Parse Structure of Paragraphs and Its Applications in Search, Engineering Applications of Artificial Intelligence, vol. 32, Jun. 2014, pp. 160-184. cited by applicant
Galitsky, Machine Learning of Syntactic Parse Trees for Search and Classification of Text, Engineering Applications of Artificial Intelligence, vol. 26, Issue 3, Mar. 2013, pp. 1072-1091. cited by applicant
Galitsky, Natural Language Question Answering System: Technique of Semantic Headers, Advanced Knowledge International, vol. 2, 2003. cited by applicant
Galitsky et al., Parse Thicket Representations for Answering Multi-Sentence Questions, International Conference on Conceptual Structures, 2013, pp. 153-172. cited by applicant
Galitsky et al., Rhetoric Map of an Answer to Compound Queries, In Proceedings of the 53rd Annual Meeting of the 20 Association for Computational Linguistics and the 7'h International Joint Conference of Natural Language Processing, Jul. 26-31, 2015, pp. 681-686. cited by applicant
Galitsky et al., Text Classification into Abstract Classes Based on Discourse Structure, Proceedings of Recent Advances in Natural Language Processing, Sep. 7-9, 2015, pp. 200-207. cited by applicant
Galitsky et al., Text Integrity Assessment: Sentiment Profile vs Rhetoric Structure, CICLing-2015, Springer international Publishing, 2015, pp. 126-139. cited by applicant
Galitsky, Using Extended Tree Kernels to Recognize Metalanguage in Text, Studies in Computational Intelligence, Feb. 2017, 26 pages. cited by applicant
Grefenstette et al., Multi-Step Regression Learning for Compositional Distributional Semantics, In Proceedings of the 26 Tenth International Conference on Computational Semantics. Association for Computational Linguistics, Mar. 2013, 11 pages. cited by applicant
Grefenstette, Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors, University of Oxford, Apr. 2013, 10 pages. cited by applicant
Grosz et al., Attention, Intentions, and the Structure of Discourse, Computational Linguistics, vol. 12, No. 3, Jul.-Sep. 1986, pp. 175-204. cited by applicant
Grosz et al., Discourse Analysis, in Understanding Spoken Language, Elsevier North-Holland, 1978, pp. 234-268. cited by applicant
Hart et al., Text Classification for Data Loss Prevention, Hewlett-Packard Development Company, L.P., 2011, 21 pages. cited by applicant
Haussler, Convolution Kernels on Discrete Structures, UCSB Technical Report, Jul. 8, 1999, 38 pages. cited by applicant
Hernault et al., A Sequential Model for Discourse Segmentation, Computational Linguistics and Intelligent Text Processing, 2010, pp. 315-326. cited by applicant
Houngbo et al., An Automated Method to Build A Corpus of Rhetorically-Classified Sentences in Biomedical Texts, Proceedings of the First Workshop on Argumentation Mining, Association for Computational Linguistics, Jun. 26, 2014, pp. 19-23. cited by applicant
Iruskieta et al., A Qualitative Comparison Method for Rhetorical Structures: Identifying Different Discourse Structures in Multilingual Corpora, Lang Resources & Evaluation, vol. 49, Issue 2, May 8, 2014, 47 pages. cited by applicant
Jansen et al., Discourse Complements Lexical Semantics for Nonfactoid Answer Reranking, In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Jun. 23-25, 2014, pp. 977-986. cited by applicant
John et al., Estimating Continuous Distributions in Bayesian Classifiers, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338-345. cited by applicant
Joty et al., A Novel Discriminative Framework for Sentence-Level Discourse Analysis, EMNLP-CoNLL'12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 904-915. cited by applicant
Joty et al., Codra: A Novel Discriminative Framework for Rhetorical Analysis, Computational Linguistics, vol. 41, No. 3, Mar. 18, 2015, pp. 385-435. cited by applicant
Joty et al., Combining Intra- and Multi-Sentential Rhetorical Parsing for Document-level Discourse Analysis, 51st Annual Meeting of the Association for Computational Linguistics, Aug. 4-9, 2013, pp. 486-496. cited by applicant
Joty et al., Discriminative Reranking of Discourse Parses Using Tree Kernels, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Oct. 25-29, 2014, pp. 2049-2060. cited by applicant
Jurafsky et al., Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Upper Saddle River, NJ: Prentice Hall, 2000, pp. 719-761. cited by applicant
Kate et al., Learning to Transform Natural to Formal Languages, In Proceedings of the Twentieth National Conference on Artificial Intelligence, Jul. 2005, pp. 1062-1068. cited by applicant
Kipper et al., A Large-scale Classification of English Verbs, Kluwer Academic Publishers, Springer Netherland, Dec. 2006, 18 pages. cited by applicant
Kipper et al., Verbnet Overview, Extensions, Mappings and Applications, Proceedings on NAACL HLT 2009: Tutorials, Association for Computational Linguistics, 2009, pp. 13-14. cited by applicant
Kirschner et al., Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications, Proceedings of the 2nd Workshop on Argumentation Mining, Jun. 4, 2015, pp. 1-11. cited by applicant
Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, International Joint Conference on Artificial Intelligence, Aug. 1995, pp. 1137-1143. cited by applicant
Kong et al., Improve Tree Kernel-Based Event Pronoun Resolution with Competitive Information, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, 2011, pp. 1814-1819. cited by applicant
Kontos et al., Question Answering and Rhetoric Analysis of Biomedical Texts in the Aroma System, National and 42 Kapodistrian University of Athens, Unpublished Manuscript, 2006, 6 pages. cited by applicant
Levinson, Presumptive Meanings: The Theory of Generalized Conversational Implicature, Cambridge, MA: The MIT Press, 2000, 11 pages. cited by applicant
Li et al., Recursive Deep Models for Discourse Parsing, Computer Science Department, 2014, 10 pages. cited by applicant
Lin et al., Recognizing Implicit Discourse Relations in the Penn Discourse Treebank, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, Aug. 6-7, 2009, pp. 343-351. cited by applicant
Litman et al., A Plan Recognition Model for Subdialogues in Conversations, Cognitive Science, 1987, pp. 163-200. cited by applicant
Mann et al., Discourse Structures for Text Generation, ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics, 1984, pp. 367-375. cited by applicant
Mann et al., Rhetorical Structure Theory and Text Analysis, University of Southern California, Nov. 1989, 66 pages. cited by applicant
Mann et al., Rhetorical Structure Theory: Towards a Functional Theory of Text Organization, Text-Interdisciplinary Journal for the Study of Discourse, vol. 8, Issue 3, Jan. 1988, pp. 243-281. cited by applicant
Mikolov et al., Distributed Representations of Words Phrases and their Compositionality, In Advances on Neural Information Processing Systems, Oct. 2013, 9 pages. cited by applicant
Mitchell et al., Composition in Distributional Models of Semantics, Cognitive Science, vol. 34, Issue 8, 2010, pp. 1388-1429. cited by applicant
Mitocariu et al., Comparing Discourse Tree Structures, Computational Linguistics and Intelligent Text Processing: 14th International Conference, vol. 7816, Mar. 24-30, 2013, 11 pages. cited by applicant
Mochales et al., Argumentation Mining, Artificial Intelligence and Law, vol. 19, Issue 1, Mar. 2011, pp. 1-22. cited by applicant
Ott et al., Finding Deceptive Opinion Spam by Any Stretch of the Imagination, In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, vol. 1, Jun. 19-24, 2011, pp. 309-319. cited by applicant
Ott et al., Negative Deceptive Opinion Spam, In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics, Jun. 9-14, 2013, pp. 497-501. cited by applicant
Peldszus et al., From Argument Diagrams to Argumentation Mining in Texts: A Survey, International Journal of Cognitive Informatics and Natural Intelligence, vol. 7, Issue 1, Jan. 2013, pp. 1-31. cited by applicant
Pendyala et al., Towards a Truthful World Wide Web from a Humanitarian Perspective, IEEE 2015 Global Humanitarian Technology Conference, Oct. 8-11, 2015, 7 pages. cited by applicant
Popescu et al., Logic-Based Rhetorical Structuring for Natural Language Generation in Human-Computer Dialogue, TSD 2007, LNAI 4629, 2007, pp. 309-317. cited by applicant
Popescu-Belis, Dialogue Acts: One or More Dimensions?, Tech Report ISSCO Working Paper No. 62, University of Geneva, 2005, 46 pages. cited by applicant
Radev et al., Centroid-Based Summarization of Multiple Documents: Sentence Extraction, Utility-Based Evaluation, 60 and User Studies, In Proceedings of the 2000 NAACL-ANLP Workshop on Automatic summarization—vol. 4, 2000, 8 pages. cited by applicant
Recasens et al., The Life and Death of Discourse Entities: Identifying Singleton Mentions, In Proceedings of NAACL-HLT, Jun. 2013, pp. 627-633. cited by applicant
Reed et al., Language Resources for Studying Argument, Proceedings of the 6th Conference on Language Resources and Evaluation, LREC2008, ELRA, 2010, pp. 2613-2618. cited by applicant
Reichman, Getting Computers to Talk Like You and Me, Discourse Context, Focus and Semantics (an ATN model) Cambridge, Mass. London: MIT Press, Jul. 1985, pp. 35-49. cited by applicant
Salton et al., Term-Weighting Approaches in Automatic Text Retrieval, Information Processing & Management, vol. 24, Issue 5, Nov. 1987, 23 pages. cited by applicant
Santhosh et al., Discourse Based Advancement on Question Answering System, International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), vol. 1, Issue 2, Oct. 2012, 12 pages. cited by applicant
Scholman et al., A Step-Wise Approach to Discourse Annotation: Towards a Reliable Categorization of Coherence 64 Relations, Categories of coherence relations in discourse annotation. Dialogue & Discourse, vol. 7, No. 2, 2016, 28 pages. cited by applicant
Searle, Speech Acts: An Essay in the Philosophy of Language, Cambridge University Press, Jan. 1969, pp. 22-53. cited by applicant
Socher et al., Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks, Stanford University, 2010, 9 pages. cited by applicant
Sperber et al., Relevance: Communication and Cognition, Blackwell, Oxford and Harvard University Press, Cambridge, MA, 1986, 331 pages. cited by applicant
Sun et al., Exploring Syntactic Structural Features for Sub-Tree Alignment Using Bilingual Tree Kernels, ACL'10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Jul. 2010, pp. 306-315. cited by applicant
Surdeanu et al., Two Practical Rhetorical Structure Theory Parsers, In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics—Human Language Technologies, Jun. 5, 2015, pp. 1-5. cited by applicant
Traum et al., Conversation Acts in Task-Oriented Spoken Dialogue, University of Rochester Computer Science, Computational Intelligence, vol. 8, No. 3, Aug. 1992, 31 pages. cited by applicant
Traum et al., Discourse Obligations in Dialogue Processing, In Proceedings of the 32nd annual meeting on Association for Computational Linguistics (ACL '94). Association for Computational Linguistics, 1994, pp. 1-8. cited by applicant
Tsui, English Conversation. Describing English Language Series, London: Oxford University Press, 1994, pp. 159-193. cited by applicant
Uliyar, A Primer: Oracle Intelligent Bots, Powered by artificial intelligence, White Paper, Sep. 2017, 28 pages. cited by applicant
Vapnik, The Nature of Statistical Learning Theory, 1995. cited by applicant
Walker et al., Quantitative and Qualitative Evaluation of Darpa Communicator Spoken Dialogue Systems, Discourse Analysis in Understanding Spoken Language, Proc. ACL, 2001, pp. 515-522. cited by applicant
Wang et al., Kernel Based Discourse Relation Recognition with Temporal Ordering Information, In Proceedings of the 48'th annual meeting of the Association for Computational Linguistics, Jul. 11-16, 2010, pp. 710-719. cited by applicant
Wu et al., Enhancing Text Representation for Classification Tasks with Semantic Graph Structures, International Journal of Innovative Computing, Information and Control, vol. 7, No. 5 (B), May 2011, pp. 2689-2698. cited by applicant
Wuchner et al., Data Loss Prevention Based on Data-Driven Usage Control, IEEE 23rd International Symposium on Software Reliability Engineering, 2012, 10 pages. cited by applicant
Yessenalina et al., Compositional Matrix-Space Models for Sentiment Analysis, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Jul. 27-31, 2011, pp. 172-182. cited by applicant
Zanzotto et al., Estimating Linear Models for Compositional Distributional Semantics, In Proceedings of the 23 International Conference on Computational Linguistics (COLING), Aug. 23-27, 2010, pp. 1263-1271. cited by applicant
Zeilenga, Lightweight Directory Access Protocol (LDAP) Read Entry Controls, IETF, RFC 4527, Jun. 2006, 8 pages. cited by applicant
Zhao et al., Facilitating Discourse Analysis with Interactive Visualization, IEEE Transactions on Visualization and Computer Graphics, vol. 18, No. 12, 2012, pp. 2639-2648. cited by applicant
“First Action Interview Pilot Program Pre-Interview Communication” issued in U.S. Appl. No. 16/145,702, mailed Feb. 7, 2019, 6 pages. cited by applicant
Chen, “Understanding Mental States in Natural Language”, Proceedings of the 8th International Conference on Computational Semantics, Jan. 2009, pp. 61-72. cited by applicant
Ebrahim, “NLP Tutorial Using Python NLTK (Simple Examples)”, DZone.com, Available Online at, URL: https://dzone.com/articles/nlp-tutorial-using-python-nltk-simple-examples, (Last Accessed Feb. 1, 2019), Sep. 24, 2017, pp. 1-10. cited by applicant
Ponti, “Machine Learning Techniques Applied to Dependency Parsing”, University of Pavia, Available Online at, URL: https://vision.unipv.it/AI/AIRG/MachineLearning TechniquesAppliedToDependencyParsingRevised.pdf (Last Accessed Jan. 1, 2019), Oct. 2015, pp. 1-45. cited by applicant
U.S. Appl. No. 16/145,702, “First Action Interview Office Action Summary”, mailed Apr. 29, 2019, 8 pages. cited by applicant
Galitsky, “Discovering Rhetorical Agreement between a Request and Response”, Dialogue & Discourse, vol. 8, No. 2, Dec. 1, 2017, pp. 167-205. cited by applicant
Galitsky et al., “On a Chat Bot Finding Answers with Optimal Rhetoric Representation”, RANLP—Recent Advances in Natural Language Processing Meet Deep Learning, Nov. 10, 2017, pp. 253-259. cited by applicant
International Application No. PCT/US2019/015696, “International Search Report and Written Opinion”, dated Apr. 23, 2019, 12 pages. cited by applicant
International Application No. PCT/US2019/031580, “International Search Report and Written Opinion”, dated Jul. 5, 2019, 12 pages. cited by applicant
U.S. Appl. No. 15/975,683, “Non-Final Office Action”, mailed Oct. 31, 2019, 27 pages. cited by applicant
U.S. Appl. No. 15/975,685, “Non-Final Office Action”, mailed Nov. 15, 2019, 23 pages. cited by applicant
U.S. Appl. No. 16/010,091, “Non-Final Office Action”, mailed Nov. 18, 2019, 26 pages. cited by applicant
U.S. Appl. No. 16/010,141, “Non-Final Office Action”, mailed Feb. 24, 2020, 12 pages. cited by applicant
U.S. Appl. No. 16/010,156, “Notice of Allowance”, mailed Feb. 6, 2020, 13 pages. cited by applicant
U.S. Appl. No. 16/010,156, “Notice of Allowance”, mailed Nov. 7, 2019, 13 pages. cited by applicant
Galitsky et al., Chatbot with a Discourse Structure-Driven Dialogue Management, Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics Available on Internet at: https://pdfs.semanticscholar.org/439f/cbe14dbc84b694bae2ee42a914d743126d12.pdf, Apr. 2017, pp. 87-90. cited by applicant
Galitsky et al., “Style and Genre Classification by Means of Deep Textual Parsing”, Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2016”, Jun. 2016, pp. 1-45. cited by applicant
Marcu et al., “An Unsupervised Approach to Recognizing Discourse Relations”, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), 2002, 8 pages. cited by applicant
International Application No. PCT/US2018/031890, “International Preliminary Report on Patentability”, mailed Nov. 21, 2019, 9 pages. cited by applicant
Tai et al., “Improved Semantic Representations from Tree-Structured Long Short-Term Memory Networks”, Available Online at: https://arxiv.org/pdf/1503.00075.pdf, May 30, 2015, 11 pages. cited by applicant
U.S. Appl. No. 15/975,683, “Non-Final Office Action”, mailed Mar. 19, 2020, 16 pages. cited by applicant
U.S. Appl. No. 15/975,685, “Non-Final Office Action”, mailed Apr. 1, 2020, 23 pages. cited by applicant
U.S. Appl. No. 16/010,091, “Notice of Allowance”, mailed Mar. 19, 2020, 13 pages. cited by applicant
U.S. Appl. No. 16/145,702, “Final Office Action”, mailed May 6, 2020, 19 pages. cited by applicant
U.S. Appl. No. 16/145,777, “Non-Final Office Action”, mailed Apr. 3, 2020, 18 pages. cited by applicant
U.S. Appl. No. 16/260,939, “Non-Final Office Action”, mailed May 1, 2020, 10 pages. cited by applicant
Galitsky et al., “Finding Maximal Common Sub-Parse Thickets for Multi-Sentence Search”, Graph Structures for Knowledge Representation and Reasoning, 2014, 19 pages. cited by applicant
Galitsky et al., “Text Classification Based on Deep Textual Parsing”, Available Online at: http://ceur-ws.org/vol-1886/paper_8.pdf, 2011, 9 pages. cited by applicant
Hara et al., “Exploring Difficulties in Parsing Imperatives and Questions”, Proceedings of the 5th International Joint Conference on Natural Language Processing, Nov. 8-13, 2011, pp. 749-757. cited by applicant
Johnson et al., “The FrameNet Tagset for Frame-Semantic and Syntactic Coding of Predicate-Argument Structure”, Proceedings of the First Meeting of the North American Chapter of the Association for Computational Linguistics, Apr. 2000, pp. 56-62. cited by applicant
International Application No. PCT/US2018/053392, “International Preliminary Report on Patentability”, mailed Apr. 9, 2020, 7 pages. cited by applicant
U.S. Appl. No. 15/975,683 , Notice of Allowance, Mailed On Jun. 12, 2020, 17 pages. cited by applicant
U.S. Appl. No. 15/975,685 , Notice of Allowance, Mailed On Jul. 24, 2020, 17 pages. cited by applicant
U.S. Appl. No. 16/010,141 , Final Office Action, Mailed On Jul. 30, 2020, 14 pages. cited by applicant
U.S. Appl. No. 16/145,702 , Notice of Allowance, Mailed On Jul. 1, 2020, 15 pages. cited by applicant
U.S. Appl. No. 16/145,777 , Notice of Allowance, Mailed On Jul. 15, 2020, 17 pages. cited by applicant
U.S. Appl. No. 16/260,930 , Non-Final Office Action, Mailed On Aug. 12, 2020, 9 pages. cited by applicant
U.S. Appl. No. 16/260,939 , Notice of Allowance, Mailed On Jun. 12, 2020, 14 pages. cited by applicant
Galitsky et al., “Finding Maximal Common Sub-Parse Thickets for Multi-Sentence Search”, IJCAI Workshop on Graphs and Knowledge Representation, IJCAI, 2013, 19 pages. cited by applicant
Kipper et al., “A Large-scale Classification of English Verbs”, Kluwer Academic Publishers, Springer Netherland, Dec. 2006, 20 pages. cited by applicant
International Application No. PCT/US2019/015696, International Preliminary Report on Patentability, Mailed On Aug. 13, 2020, 8 pages. cited by applicant
International Application No. PCT/US2019/031580, International Preliminary Report on Patentability, Mailed On Nov. 19, 2020, 8 pages. cited by applicant
Mathkour, “A Novel Rhetorical Structure Approach for Classifying Arabic Security Documents”, International Journal of Computer Theory and Engineering, vol. 1(3), Aug. 2009, 1793-8201. cited by applicant
U.S. Appl. No. 16/408,224, Supplemental Notice of Allowability mailed on Apr. 7, 2022, 4 pages. cited by applicant
U.S. Appl. No. 16/408,224, Supplemental Notice of Allowability mailed on Feb. 15, 2022, 4 pages. cited by applicant
U.S. Appl. No. 16/902,015, First Action Interview Office Action Summary mailed on Mar. 11, 2022, 5 pages. cited by applicant
U.S. Appl. No. 16/902,015, First Action Interview Pilot Program Pre-Interview Communication mailed on Jan. 21, 2022, 5 pages. cited by applicant
European Application No. 18789298.9, Office Action mailed on Jan. 18, 2022, 8 pages. cited by applicant
Indian Application No. 202047007045, First Examination Report mailed on Mar. 25, 2022, 6 pages. cited by applicant
U.S. Appl. No. 17/084,116, First Action Interview Pilot Program Pre-Interview Communication mailed on Sep. 28, 2022, 6 pages. cited by applicant
Indian Application No. 202047043656, First Examination Report mailed on Sep. 1, 2022, 6 pages. cited by applicant
U.S. Appl. No. 16/902,015, Notice of Allowance mailed on Oct. 4, 2022, 9 pages. cited by applicant
Japan Application No. 2020-517950, Notice of Decision to Grant mailed on Nov. 1, 2022, 3 pages. cited by applicant
Miyabe et al., Identifying a cross-document relation between sentences, IPSJ SIG Technical Reports, Information Processing Society of Japan, vol. 2005, No. 73, pp. 35-42. cited by applicant
Japanese Patent Application No. 2020-517950, Office Action mailed on Jul. 12, 2022, 5 pages. cited by applicant
U.S. Appl. No. 16/902,015, Final Office Action mailed on Jun. 9, 2022, 13 pages. cited by applicant
U.S. Appl. No. 17/084,116, Notice of Allowance, Mailed On Nov. 30, 2022, 17 pages. cited by applicant
Application No. CN201980030899.2 , Office Action, Mailed On Jul. 1, 2024, 5 pages. cited by applicant
Application No. JP2022-191911 , Office Action, Mailed On Jun. 18, 2024, 2 pages. cited by applicant
Application No. JP2023-060794 , Notice of Decision to Grant, Mailed On Jul. 2, 2024, 3 pages. cited by applicant

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