Combining Lexical Substitutes in Neural Word Sense Induction

Nikolay Arefyev, Boris Sheludko, Alexander Panchenko


Abstract
Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models. Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach. Our simple yet efficient improvement establishes a new state-of-the-art on WSI datasets for two languages. Besides, we show improvements to the original approach on a lexical substitution dataset.
Anthology ID:
R19-1008
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
62–70
Language:
URL:
https://www.aclweb.org/anthology/R19-1008
DOI:
10.26615/978-954-452-056-4_008
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1008.pdf