Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks

Arkadiusz Janz, Maciej Piasecki


Abstract
Word Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses. Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios. In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks. We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used. The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Składnica, KPWr) datasets.
Anthology ID:
R19-1061
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
516–525
Language:
URL:
https://aclanthology.org/R19-1061
DOI:
10.26615/978-954-452-056-4_061
Bibkey:
Cite (ACL):
Arkadiusz Janz and Maciej Piasecki. 2019. Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 516–525, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks (Janz & Piasecki, RANLP 2019)
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PDF:
https://preview.aclanthology.org/author-url/R19-1061.pdf