Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT

DOI

10.2991/ijcis.d.200423.001How to use a DOI?

Keywords

Sentiment classification; Grammar rules; BERT; Bi-GRU

Abstract

Sentiment classification is a fundamental task in NLP, and its aim to predict the sentiment polarities of the given texts. Recent researches show great interest in modeling Chinese sentiment classification. However, the complexity of Chinese grammar makes the performance of the existing Chinese sentiment classification model not perform well. In order to address the above problem, we propose a sentiment classification method based on grammar rules and bidirectional encoder representation from transformers (BERT). We first preprocess data through BERT model. Then we combine the Chinese grammar rules with Bi-gated recurrent neural network (GRU) in the form of constraints, and simulate the linguistic functions at the sentence by standardizing the output of adjacent positions. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed method, and our findings in the experiment provide new insights for the future development of Chinese sentiment classification.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Qiang Lu
AU  - Zhenfang Zhu
AU  - Fuyong Xu
AU  - Dianyuan Zhang
AU  - Wenqing Wu
AU  - Qiangqiang Guo
PY  - 2020
DA  - 2020/05/22
TI  - Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT
JO  - International Journal of Computational Intelligence Systems
SP  - 538
EP  - 548
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200423.001
DO  - 10.2991/ijcis.d.200423.001
ID  - Lu2020
ER  -