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.
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Notes
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The sense inventory is from WordNet3.0 (wordnet.princeton.edu/)
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Acknowledgement
This work is supported by the Fundamental Research Funds for the Central Universities, SCUT (Nos. 2017ZD048, 2015ZM136), Tiptop Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2015TQ01X633), Science and Technology Planning Project of Guangdong Province, China (No. 2016A030310423), Science and Technology Program of Guangzhou (International Science & Technology Cooperation Program No. 201704030076) and Science and Technology Planning Major Project of Guangdong Province (No. 2015A070711001), the Start-Up Research Grant (RG 37/2016-2017R), and a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16). This work is also partially supported by a CUHK Direct Grant for Research (Project Code EE16963).
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Lei, X., Cai, Y., Li, Q., Xie, H., Leung, Hf., Wang, F.L. (2017). Combining Local and Global Features in Supervised Word Sense Disambiguation. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_10
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