Word Sense Disambiguation Based on Convolution Neural Network

doi:10.13190/j.jbupt.2018-148

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (3): 114-119.doi: 10.13190/j.jbupt.2018-148

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Word Sense Disambiguation Based on Convolution Neural Network

ZHANG Chun-xiang¹, ZHAO Ling-yun², GAO Xue-yao²

1. School of Software and Microelectronics, Harbin University of Science and Technology, Harbin 150080, China;
2. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China

Received:2018-07-11 Online:2019-06-28 Published:2019-06-20

Abstract

Abstract: In order to improve the performance of word sense disambiguation (WSD), a disambiguation method based on convolution neural network (CNN) is proposed. Ambiguous word is viewed as center and four adjacent word units around its left and right sides are extended. Word, part-of-speech and semantic categories are extracted as disambiguation features. Based on disambiguation features, CNN is used to determine semantic categories of ambiguous words. Training corpus of SemEval-2007:Task#5 and semantic annotation corpus from Harbin Institute of Technology are used to optimize CNN classifier. Testing corpus of SemEval-2007:Task#5 is used to test the performance of WSD classifier. Average disambiguation accuracy of the proposed method is improved. Experiments show that this method is feasible in WSD.

Key words: word sense disambiguation, convolution neural network, disambiguation features, semantic categories

CLC Number:

TP391.2

ZHANG Chun-xiang, ZHAO Ling-yun, GAO Xue-yao. Word Sense Disambiguation Based on Convolution Neural Network[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(3): 114-119.

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Word Sense Disambiguation Based on Convolution Neural Network

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