Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph

Dongsuk O, Sunjae Kwon, Kyungsun Kim, Youngjoong Ko


Abstract
Word sense disambiguation (WSD) is the task to determine the word sense according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it has fewer word set constraints than supervised approaches requiring training data. In this paper, we propose a new WSD method to generate the context of an ambiguous word by using similarities between an ambiguous word and words in the input document. In addition, to leverage our WSD method, we further propose a new word similarity calculation method based on the semantic network structure of BabelNet. We evaluate the proposed methods on the SemEval-13 and SemEval-15 for English WSD dataset. Experimental results demonstrate that the proposed WSD method significantly improves the baseline WSD method. Furthermore, our WSD system outperforms the state-of-the-art WSD systems in the Semeval-13 dataset. Finally, it has higher performance than the state-of-the-art unsupervised knowledge-based WSD system in the average performance of both datasets.
Anthology ID:
C18-1229
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2704–2714
Language:
URL:
https://aclanthology.org/C18-1229
DOI:
Bibkey:
Cite (ACL):
Dongsuk O, Sunjae Kwon, Kyungsun Kim, and Youngjoong Ko. 2018. Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2704–2714, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph (O et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1229.pdf
Code
 nlpbank/SRP2Vec