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    <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).</abstract>
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%0 Conference Proceedings
%T SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations
%A Maru, Marco
%A Scozzafava, Federico
%A Martelli, Federico
%A Navigli, Roberto
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F maru-etal-2019-syntagnet
%X 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).
%R 10.18653/v1/D19-1359
%U https://aclanthology.org/D19-1359
%U https://doi.org/10.18653/v1/D19-1359
%P 3534-3540

Markdown (Informal)

[SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations](https://aclanthology.org/D19-1359) (Maru et al., EMNLP-IJCNLP 2019)

• SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations (Maru et al., EMNLP-IJCNLP 2019)

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.