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2017 | OriginalPaper | Buchkapitel

Combining Local and Global Features in Supervised Word Sense Disambiguation

verfasst von : Xue Lei, Yi Cai, Qing Li, Haoran Xie, Ho-fung Leung, Fu Lee Wang

Erschienen in: Web Information Systems Engineering – WISE 2017

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Eliminating Incorrect Cross-Language Links in Wikipedia

Nächstes Kapitel A Concurrent Interdependent Service Level Agreement Negotiation Protocol in Dynamic Service-Oriented Computing Environments

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Titel: Combining Local and Global Features in Supervised Word Sense Disambiguation
verfasst von: Xue Lei
Yi Cai
Qing Li
Haoran Xie
Ho-fung Leung
Fu Lee Wang
Verlag: Springer International Publishing
Buch: Web Information Systems Engineering – WISE 2017
Print ISBN: 978-3-319-68785-8

Electronic ISBN: 978-3-319-68786-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68786-5_10

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