Sprinkled semantic diffusion kernel for word sense disambiguation

Wang, T; Li, W; Liu, F; Hua, J

Sprinkled semantic diffusion kernel for word sense disambiguation

Wang, T Li, W Liu, F Hua, J

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Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2017, 64 pp. 43 - 51
Issue Date:: 2017-09-01

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Field	Value	Language
dc.contributor.author	Wang, T	en_US
dc.contributor.author	Li, W	en_US
dc.contributor.author	Liu, F	en_US
dc.contributor.author	Hua, J	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2017, 64 pp. 43 - 51	en_US
dc.identifier.issn	0952-1976	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127038
dc.description.abstract	© 2017 Elsevier Ltd Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing (NLP). In this paper, we are concerned with kernel methods for automatic WSD. Under this framework, the main difficulty is to design an appropriate kernel function to represent the sense distinction knowledge. Semantic diffusion kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information to smooth the typical “Bag of Words” (BOW) representation, has been successfully applied to WSD. However, the diffusion is an unsupervised process, which fails to exploit the class information in a supervised classification scenario. To address the limitation, we present a sprinkled semantic diffusion kernel to make use of the class knowledge of training documents in addition to the co-occurrence knowledge. The basic idea is to construct an augmented term-document matrix by encoding class information as additional terms and appending them to training documents. Diffusion is then performed on the augmented term-document matrix. In this way, the words belonging to the same class are indirectly drawn closer to each other, hence the class-specific word correlations are strengthened. We evaluate our method on several Senseval/Semeval benchmark examples with support vector machine (SVM), and show that the proposed kernel can significantly improve the disambiguation performance over semantic diffusion kernel in terms of different measures and yield a competitive result with the state-of-the-art kernel methods for WSD.	en_US
dc.relation.ispartof	Engineering Applications of Artificial Intelligence	en_US
dc.relation.isbasedon	10.1016/j.engappai.2017.05.010	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Sprinkled semantic diffusion kernel for word sense disambiguation	en_US
dc.type	Journal Article
utslib.citation.volume	64	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	64	en_US

Abstract:

© 2017 Elsevier Ltd Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing (NLP). In this paper, we are concerned with kernel methods for automatic WSD. Under this framework, the main difficulty is to design an appropriate kernel function to represent the sense distinction knowledge. Semantic diffusion kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information to smooth the typical “Bag of Words” (BOW) representation, has been successfully applied to WSD. However, the diffusion is an unsupervised process, which fails to exploit the class information in a supervised classification scenario. To address the limitation, we present a sprinkled semantic diffusion kernel to make use of the class knowledge of training documents in addition to the co-occurrence knowledge. The basic idea is to construct an augmented term-document matrix by encoding class information as additional terms and appending them to training documents. Diffusion is then performed on the augmented term-document matrix. In this way, the words belonging to the same class are indirectly drawn closer to each other, hence the class-specific word correlations are strengthened. We evaluate our method on several Senseval/Semeval benchmark examples with support vector machine (SVM), and show that the proposed kernel can significantly improve the disambiguation performance over semantic diffusion kernel in terms of different measures and yield a competitive result with the state-of-the-art kernel methods for WSD.

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http://hdl.handle.net/10453/127038