Tingxin Wei1, 2, Weiguang Qu2, 3, *, Junsheng Zhou3, Yunfei Long4, Yanhui Gu3, Zhentao Xia3
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1897-1913, 2020, DOI:10.32604/cmc.2020.010813
Abstract The meaning of a word includes a conceptual meaning and a distributive
meaning. Word embedding based on distribution suffers from insufficient conceptual
semantic representation caused by data sparsity, especially for low-frequency words. In
knowledge bases, manually annotated semantic knowledge is stable and the essential
attributes of words are accurately denoted. In this paper, we propose a Conceptual
Semantics Enhanced Word Representation (CEWR) model, computing the synset
embedding and hypernym embedding of Chinese words based on the Tongyici Cilin
thesaurus, and aggregating it with distributed word representation to have both distributed
information and the conceptual meaning encoded in the representation of… More >
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