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Abstract
In recent years, word embeddings have been shown to improve the performance in NLP tasks such as syntactic parsing or sentiment analysis. While useful, they are problematic in representing ambiguous words with multiple meanings, since they keep a single representation for each word in the vocabulary. Constructing separate embeddings for meanings of ambiguous words could be useful for solving the Word Sense Disambiguation (WSD) task. In this work, we present how a word embeddings averagebased method can be used to produce semantic-rich meaning embeddings. We also open-source a WSD dataset that was created for the purpose of evaluating methods presented in this research.
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Authors (5)
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Grzegorz Beringer
-
Mateusz Jabłoński
-
Piotr Januszewski
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.15439/2019F120
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Annals of Computer Science and Information Systems
no. 18,
pages 273 - 276,
ISSN: 2300-5963 - Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Beringer G., Jabłoński M., Januszewski P., Sobecki A., Szymański J.: Towards semantic-rich word embeddings// Annals of Computer Science and Information Systems -Vol. 18, (2019), s.273-276
- DOI:
- Digital Object Identifier (open in new tab) 10.15439/2019f120
- Verified by:
- Gdańsk University of Technology
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