Feature2vec: Distributional semantic modelling of human property knowledge

Steven Derby, Paul Miller, Barry Devereux

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)
113 Downloads (Pure)

Abstract

Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neurolinguistic research on semantic cognition. However, these datasets are limited in size due to practical obstacles associated with exhaustively listing properties for a large number of words. In contrast, the development of distributional modelling techniques and the availability of vast text corpora have allowed researchers to construct effective vector space models of word meaning over large lexicons. However, this comes at the cost of interpretable, human-like information about word meaning. We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features. Our approach gives a measure of concept and feature affinity in a single semantic space, which makes for easy and efficient ranking of candidate human-derived semantic properties for arbitrary words. We compare our model with a previous approach, and show that it performs better on several evaluation tasks. Finally, we discuss how our method could be used to develop efficient sampling techniques to extend existing feature norm datasets in a reliable way.
Original languageEnglish
Title of host publicationProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
PublisherAssociation for Computational Linguistics
Pages5853-5859
Number of pages7
ISBN (Print)9781950737901
DOIs
Publication statusPublished - 2020
EventConference on Empirical Methods in Natural Language Processing & International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019) - Hong Kong, Hong Kong
Duration: 03 Nov 201907 Nov 2019
https://www.emnlp-ijcnlp2019.org/

Publication series

NameEMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference

Conference

ConferenceConference on Empirical Methods in Natural Language Processing & International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019)
Abbreviated titleEMNLP-IJCNLP 2019
CountryHong Kong
CityHong Kong
Period03/11/201907/11/2019
Internet address

Keywords

  • natural language processing
  • semantics
  • lexical semantics

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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