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  • Open Access


    Enhancing Embedding-Based Chinese Word Similarity Evaluation with Concepts and Synonyms Knowledge

    Fulian Yin, Yanyan Wang, Jianbo Liu*, Meiqi Ji

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 747-764, 2020, DOI:10.32604/cmes.2020.010579

    Abstract Word similarity (WS) is a fundamental and critical task in natural language processing. Existing approaches to WS are mainly to calculate the similarity or relatedness of word pairs based on word embedding obtained by massive and high-quality corpus. However, it may suffer from poor performance for insuf- ficient corpus in some specific fields, and cannot capture rich semantic and sentimental information. To address these above problems, we propose an enhancing embedding-based word similarity evaluation with character-word concepts and synonyms knowledge, namely EWS-CS model, which can provide extra semantic information to enhance word similarity evaluation. The More >

  • Open Access


    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 More >

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