Embeddings for Word Sense Disambiguation: An Evaluation Study
| Autor: | Mohammad Taher Pilehvar, Roberto Navigli, Ignacio Iacobacci |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Word-sense disambiguation
business.industry Computer science 02 engineering and technology computer.software_genre SemEval 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Affect (linguistics) business computer Natural language processing Word (computer architecture) |
| Zdroj: | ACL (1) |
| DOI: | 10.18653/v1/p16-1085 |
| Popis: | Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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