On learning and representing social meaning in NLP: a sociolinguistic perspective

Dong Nguyen, Laura Rosseel, Jack Grieve

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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Abstract

The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.
Original languageEnglish
Title of host publicationProceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
EditorsKristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
PublisherAssociation for Computational Linguistics
Pages603-612
Number of pages10
Publication statusPublished - 2021
Event2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Online
Duration: 6 Jun 202111 Jun 2021
Conference number: 2021
https://2021.naacl.org/

Conference

Conference2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Abbreviated titleNAACL
Period6/06/2111/06/21
Internet address

Keywords

  • NLP
  • social meaning
  • word embeddings
  • natural language processing
  • language variation
  • spelling variation

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