%0 Journal Article
%A GAO Xue-yao
%A ZHANG Chun-xiang
%A ZHAO Ling-yun
%T Word Sense Disambiguation Based on Convolution Neural Network
%D 2019
%R 10.13190/j.jbupt.2018-148
%J Journal of Beijing University of Posts and Telecommunications
%P 114-119
%V 42
%N 3
%X 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.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-148