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    Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

    Wenpeng Lu1,*, Fanqing Meng2, Shoujin Wang3, Guoqiang Zhang4, Xu Zhang1, Antai Ouyang5, Xiaodong Zhang6

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 197-212, 2019, DOI:10.32604/cmc.2019.06068

    Abstract Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet. Then, English word similarity… More >

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