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SINr: a python package to train interpretable word and graph embeddings

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  • معلومة اضافية
    • Contributors:
      Laboratoire d'Informatique de l'Université du Mans (LIUM); Le Mans Université (UM); Equipe Language and Speech Technology (LST); Le Mans Université (UM)-Le Mans Université (UM); Laboratoire d'Informatique Fondamentale d'Orléans (LIFO); Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA); ANR-21-CE23-0010,DIGING,Plongements lexicaux temporels et dynamiques basés graphes(2021)
    • بيانات النشر:
      CCSD
    • الموضوع:
      2023
    • Collection:
      Université d'Orléans: HAL
    • الموضوع:
      Le Havre; France
    • نبذة مختصرة :
      International audience ; In this paper, we introduce the SINr Python package to train word and graph embeddings. The SINr approach is based on community detection: a vector for a node is built upon the distribution of its connections through the communities detected on the graph at hand. Because of this, the algorithm runs very fast, and does not require GPUs to proceed. Furthermore, the dimensions of the embedding space are interpretable, those are based on the communities extracted. The package is distributed under Cecill-2.1 license and is available on Github and pypi.
    • الرقم المعرف:
      10.5281/zenodo.7957531
    • الدخول الالكتروني :
      https://hal.science/hal-04113024
      https://hal.science/hal-04113024v1/document
      https://hal.science/hal-04113024v1/file/FRCSS_2023___SINr_library-2.pdf
      https://doi.org/10.5281/zenodo.7957531
    • Rights:
      http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.2B860CED