Hanyu Shi1, Weiguang Qu1,2,*, Tingxin Wei2,3, Junsheng Zhou1, Yunfei Long4, Yanhui Gu1, Bin Li2
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4113-4127, 2021, DOI:10.32604/cmc.2021.019518
Abstract In Mandarin Chinese, when the noun head appears in the context, a quantity noun phrase can be reduced to a quantity phrase with the noun head omitted. This phrase structure is called elliptical quantity noun phrase. The automatic recovery of elliptical quantity noun phrase is crucial in syntactic parsing, semantic representation and other downstream tasks. In this paper, we propose a hybrid neural network model to identify the semantic category for elliptical quantity noun phrases and realize the recovery of omitted semantics by supplementing concept categories. Firstly, we use BERT to generate character-level vectors. Secondly, Bi-LSTM is applied to capture… More >
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