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Towards an evolutionary-based approach for natural language processing

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  • معلومة اضافية
    • Contributors:
      Manzoni, L; Jakobovic, D; Mariot, L; Picek, S; Castelli, M
    • بيانات النشر:
      Association for Computing Machinery
    • الموضوع:
      2020
    • Collection:
      Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive)
    • نبذة مختصرة :
      Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a first proof-of-concept that combines GP with the well established NLP tool word2vec for the next word prediction task. The main idea is that, once words have been moved into a vector space, traditional GP operators can successfully work on vectors, thus producing meaningful words as the output. To assess the suitability of this approach, we perform an experimental evaluation on a set of existing newspaper headlines. Individuals resulting from this (pre-)training phase can be employed as the initial population in other NLP tasks, like sentence generation, which will be the focus of future investigations, possibly employing adversarial co-evolutionary approaches.
    • Relation:
      info:eu-repo/semantics/altIdentifier/isbn/9781450371285; info:eu-repo/semantics/altIdentifier/wos/WOS:000605292300114; ispartofbook:GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference; 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - 8 July 2020 through 12 July 2020; firstpage:985; lastpage:993; numberofpages:9; https://hdl.handle.net/10281/501799; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85091765216
    • الرقم المعرف:
      10.1145/3377930.3390248
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.DFA7141