Word attribute prediction enhanced by lexical entailment tasks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human semantic knowledge about concepts acquired through perceptual inputs and daily experiences can be expressed as a bundle of attributes. Unlike the conventional distributed word representations that are purely induced from a text corpus, a semantic attribute is associated with a designated dimension in attribute-based vector representations. Thus, semantic attribute vectors can effectively capture the commonalities and differences among concepts. However, as semantic attributes have been generally created by psychological experimental settings involving human annotators, an automatic method to create or extend such resources is highly demanded in terms of language resource development and maintenance. This study proposes a two-stage neural network architecture, Word2Attr, in which initially acquired attribute representations are then fine-tuned by employing supervised lexical entailment tasks. The quantitative empirical results demonstrated that the fine-tuning was indeed effective in improving the performances of semantic/visual similarity/relatedness evaluation tasks. Although the qualitative analysis confirmed that the proposed method could often discover valid but not-yet human-annotated attributes, they also exposed future issues to be worked: we should refine the inventory of semantic attributes that currently relies on an existing dataset.

Original languageEnglish
Title of host publicationLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
EditorsNicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
PublisherEuropean Language Resources Association (ELRA)
Pages5846-5854
Number of pages9
ISBN (Electronic)9791095546344
Publication statusPublished - 2020
Event12th International Conference on Language Resources and Evaluation, LREC 2020 - Marseille, France
Duration: 2020 May 112020 May 16

Publication series

NameLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

Conference

Conference12th International Conference on Language Resources and Evaluation, LREC 2020
Country/TerritoryFrance
CityMarseille
Period20/5/1120/5/16

Keywords

  • Fine-tuning
  • Lexical entailment
  • Similarity
  • Word attribute prediction
  • Word attributes

ASJC Scopus subject areas

  • Language and Linguistics
  • Education
  • Library and Information Sciences
  • Linguistics and Language

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