Embedding Structured Dictionary Definitions

Steven R. Wilson, Walid Magdy, Barbara McGillivray, Gareth Tyson

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

Abstract

Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords. Our work generalizes several existing models for learning word embeddings from dictionaries. However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries.
Original languageEnglish
Title of host publicationProceedings of the First Workshop on Insights from Negative Results in NLP
PublisherAssociation for Computational Linguistics (ACL)
Pages117-125
Number of pages9
ISBN (Electronic)978-1-952148-66-8
Publication statusPublished - 19 Nov 2020
EventWorkshop on Insights from Negative Results in NLP - Virtual event
Duration: 19 Nov 202019 Nov 2020
https://insights-workshop.github.io/index

Workshop

WorkshopWorkshop on Insights from Negative Results in NLP
Abbreviated titleINSIGHTS 2020
CityVirtual event
Period19/11/2019/11/20
Internet address

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