Literature DB >> 34666227

Distributional social semantics: Inferring word meanings from communication patterns.

Brendan T Johns1.   

Abstract

Distributional models of lexical semantics have proven to be powerful accounts of how word meanings are acquired from the natural language environment (Günther, Rinaldi, & Marelli, 2019; Kumar, 2020). Standard models of this type acquire the meaning of words through the learning of word co-occurrence statistics across large corpora. However, these models ignore social and communicative aspects of language processing, which is considered central to usage-based and adaptive theories of language (Tomasello, 2003; Beckner et al., 2009). Johns (2021) recently demonstrated that integrating social and communicative information into a lexical strength measure allowed for benchmark fits to be attained for lexical organization data, indicating that the social world contains important statistical information for language learning and processing. Through the analysis of the communication patterns of over 330,000 individuals on the online forum Reddit, totaling approximately 55 billion words of text, the findings of the current article demonstrates that social information about word usage allows for unique aspects of a word's meaning to be acquired, providing a new pathway for distributional model development.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Big data; Cognitive modeling; Distributional modeling; Lexical semantics; Machine learning

Mesh:

Year:  2021        PMID: 34666227     DOI: 10.1016/j.cogpsych.2021.101441

Source DB:  PubMed          Journal:  Cogn Psychol        ISSN: 0010-0285            Impact factor:   3.468


  1 in total

1.  Context Availability and Sentence Availability Ratings for 3,000 English Words and their Association with Lexical Processing.

Authors:  Ellen Taylor; Kate Nation; Yaling Hsiao
Journal:  J Cogn       Date:  2022-03-09
  1 in total

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