Automatically Linking Lexical Resources with Word Sense Embedding Models

Luis Nieto-Piña, Richard Johansson


Abstract
Automatically learnt word sense embeddings are developed as an attempt to refine the capabilities of coarse word embeddings. The word sense representations obtained this way are, however, sensitive to underlying corpora and parameterizations, and they might be difficult to relate to formally defined word senses. We propose to tackle this problem by devising a mechanism to establish links between word sense embeddings and lexical resources created by experts. We evaluate the applicability of these links in a task to retrieve instances of word sense unlisted in the lexicon.
Anthology ID:
W18-4003
Volume:
Proceedings of the Third Workshop on Semantic Deep Learning
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Editors:
Luis Espinosa Anke, Dagmar Gromann, Thierry Declerck
Venue:
SemDeep
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
23–29
Language:
URL:
https://aclanthology.org/W18-4003
DOI:
Bibkey:
Cite (ACL):
Luis Nieto-Piña and Richard Johansson. 2018. Automatically Linking Lexical Resources with Word Sense Embedding Models. In Proceedings of the Third Workshop on Semantic Deep Learning, pages 23–29, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Automatically Linking Lexical Resources with Word Sense Embedding Models (Nieto-Piña & Johansson, SemDeep 2018)
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PDF:
https://aclanthology.org/W18-4003.pdf