A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

Abstract

Object-verb (OV) inflections are an important grammatical device in Chinese with denotative utility. The decorativeness of OV inflections shows different levels in Chinese: OV nouns denote things in reality and are the most denotative; OV independent structures can denote both denotation and trait; OV structures that are definite are not self-sufficient and denote a certain trait. The verb category of V is worn out in denotative OV structures, and OV phrases must repair the wear and tear of V’s category and enhance V’s declarativity when forming small sentences. In this paper, we propose an attention-based approach to Chinese stance representation based on modern Chinese OV order types; firstly, we use bidirectional (bidirectional) long and short-term memory neural networks (LSTM) and convolutional neural networks (CNNs) to obtain text representation vectors and local convolutional features, respectively, and then, we use attention mechanisms to add influence weight information to the local convolutional features and finally fuse the two features for classification. Experiments on the relevant corpus show that the method achieves better stance representation results, and the addition of attention mechanisms can effectively improve the accuracy of stance representation.