计算机工程与应用2024，Vol.60Issue(20)：153-159,7.DOI:10.3778/j.issn.1002-8331.2307-0015
融合BERT和双向长短时记忆网络的中文反讽识别研究
Research on Chinese Irony Recognition by Integrating BERT and Bidirectional Long Short-Term Memory Networks
摘要
Abstract
Users comment on hot topics on microblog using rhetorical techniques such as irony and sarcasm.Irony and sarcasm themselves carry a certain emotional tendency,which has a certain tendency to affect the sentiment analysis results.Therefore,this paper focuses on irony recognition of Chinese microblog comments,constructs a tri-classified dataset con-taining ironic,sarcasm and non-irony,and proposes a model BERT_BiLSTM based on bidirectional encoder representa-tions from Transformers(BERT)and bidirectional long short-term memory network(BiLSTM).The model generates dynamic word vectors containing contextual information through BERT,inputs BiLSTM to extract the deep ironic fea-tures of the text,and passes in softmax at the fully connected layer for ironic recognition of the text.The experimental results indicate that the BERT_BiLSTM model proposed in this paper has significantly improved the accuracy and F1 values compared with the existing mainstream models on both binary and triple classification datasets.关键词
反讽识别/BERT/特征提取/双向长短时记忆网络(BiLSTM)Key words
irony recognition/bidirectional encoder representations from Transformers(BERT)/feature extraction/bidi-rectional long short-term memory network(BiLSTM)分类
信息技术与安全科学引用本文复制引用
王旭阳,戚楠,魏申酉..融合BERT和双向长短时记忆网络的中文反讽识别研究[J].计算机工程与应用,2024,60(20):153-159,7.基金项目
国家自然科学基金(62161019). （62161019）
