SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations

Marco Maru, Federico Scozzafava, Federico Martelli, Roberto Navigli


Abstract
Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. This paper introduces SyntagNet, a novel resource consisting of manually disambiguated lexical-semantic combinations. By capturing sense distinctions evoked by syntagmatic relations, SyntagNet enables knowledge-based WSD systems to establish a new state of the art which challenges the hitherto unrivaled performances attained by supervised approaches. To the best of our knowledge, SyntagNet is the first large-scale manually-curated resource of this kind made available to the community (at http://syntagnet.org).
Anthology ID:
D19-1359
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3534–3540
Language:
URL:
https://aclanthology.org/D19-1359
DOI:
10.18653/v1/D19-1359
Bibkey:
Cite (ACL):
Marco Maru, Federico Scozzafava, Federico Martelli, and Roberto Navigli. 2019. SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3534–3540, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations (Maru et al., EMNLP-IJCNLP 2019)
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PDF:
https://aclanthology.org/D19-1359.pdf