ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||5|
|Section||Session 1: Robotics|
|Published online||05 September 2017|
Chinese Word Sense Disambiguation using a LSTM
National Key Lab of Parallel and Distributed Computing, National University of Defense Technology, Changsha, China
Word sense disambiguation (WSD) is a challenging natural language processing (NLP) problem. We propose a new strategy for WSD, which at first replaces the interesting word in a sentence by the different synonyms corresponding to the different meanings, and then justify whether the transformed sentence is “legal”. A legal sentence is still legal after one or more word are replaced by other ones with the same meaning. A long short-term memory (LSTM) network-based model is proposed to perform the sentence/text classification. Furthermore, we build a Chinese WSD dataset based on HIT-CIR Tongyici Cilin (Extended) dataset. The model is evaluated on the new dataset and achieves better performance than the state-of-the-art.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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