Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation

Alexander Panchenko, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann

Abstract
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.
Anthology ID:
W17-1909
Volume:
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Jose Camacho-Collados, Mohammad Taher Pilehvar
Venue:
SENSE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
72–78
Language:
URL:
https://aclanthology.org/W17-1909
DOI:
10.18653/v1/W17-1909
Bibkey:
Cite (ACL):
Alexander Panchenko, Stefano Faralli, Simone Paolo Ponzetto, and Chris Biemann. 2017. Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 72–78, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation (Panchenko et al., SENSE 2017)
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https://preview.aclanthology.org/add_acl24_videos/W17-1909.pdf
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