Perceptually grounded selectional preferences

Ekaterina Shutova, Niket Tandon, Gerard De Melo

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

5 Scopus citations

Abstract

Selectional preferences (SPs) are widely used in NLP as a rich source of semantic information. While SPs have been traditionally induced from textual data, human lexical acquisition is known to rely on both linguistic and perceptual experience. We present the first SP learning method that simultaneously draws knowledge from text, images and videos, using image and video descriptions to obtain visual features. Our results show that it outperforms linguistic and visual models in isolation, as well as the existing SP induction approaches.

Original languageEnglish (US)
Title of host publicationACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages950-960
Number of pages11
ISBN (Electronic)9781941643723
DOIs
StatePublished - 2015
Externally publishedYes
Event53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 - Beijing, China
Duration: Jul 26 2015Jul 31 2015

Publication series

NameACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference
Volume1

Other

Other53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015
Country/TerritoryChina
CityBeijing
Period7/26/157/31/15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

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