Combining local and global features in supervised word sense disambiguation
Author(s)
Xie, Haoran
Wang, Philips Fu Lee
Author(s)
Lei, X.
Cai, Y.
Li, Q.
Leung, H.-F.
Date Issued
2017
Publisher
Springer
Related Publication(s)
Web Information Systems Engineering (18th International Conference, WISE 2017) Proceedings, Part II
Start page
117
End page
131
Abstract
Word Sense Disambiguation (WSD) is a task to identify the sense of a polysemy in given context. Recently, word embeddings are applied to WSD, as additional input features of a supervised classifier. However, previous approaches narrowly use word embeddings to represent surrounding words of target words. They may not make sufficient use of word embeddings in representing different features like dependency relations, word order and global contexts (the whole document). In this work, we combine local and global features to perform WSD. We explore utilizing word embeddings to leverage word order and dependency features. We also use word embeddings to represent global contexts as global features. We conduct experiments to evaluate our methods and find out that our methods outperform the state-of-the-art methods on Lexical Sample WSD datasets.
SFU Affiliated Publication
Yes
Availability at SFU Library
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