文献类型：期刊文献

英文题名：Automatic classification of interactive texts in online collaborative discussion based on multi-feature fusion

作者：Li, Shuhong[1];Deng, Mingming[1];Shao, Zheng[1];Chen, Xu[1];Zheng, Yafeng[2]

第一作者：李淑红

通讯作者：Zheng, YF[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou, Peoples R China;[2]Beijing Normal Univ Zhuhai, Inst Adv Studies Humanities & Social Sci, Ctr Educ Sci & Technol, Zhuhai, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Beijing Normal Univ Zhuhai, Inst Adv Studies Humanities & Social Sci, Ctr Educ Sci & Technol, Zhuhai, Peoples R China.

年份：2023

卷号：107

外文期刊名：COMPUTERS & ELECTRICAL ENGINEERING

收录：;EI(收录号：20231013678120);Scopus(收录号：2-s2.0-85149288210);WOS:【SCI-EXPANDED(收录号:WOS:000951857100001)】；

基金：This research has been supported by the National Natural Science Foundation of China (Grant Nos. 61907011 and 62077005) and the Henan science and technology project (No. 222102210326) .

语种：英文

外文关键词：Online collaborative discussion; Multi-feature fusion; BERT; CNN; BiLSTM

摘要：The recognition of learners' speech intention in the online collaborative learning scene is of great significance for exploring the rules of knowledge construction such as knowledge development and emotional communication in the collaborative process. The essence of speech intention recognition is text classification. At present, text classification is mostly based on deep learning model. However, online collaborative discussion has the characteristics of strong contextual se-mantic relationship and key characteristic words. When only the deep learning method is used for text classification, there may be insufficient acquisition of contextual semantic relations and neglect of key feature words, resulting in a decline in the accuracy of classification results. Therefore, this paper proposes a multi-feature fusion model, which uses BERT to represent the text as a word vector, BiLSTM to extract the context features of the text, CNN to extract the local features of the text, and average pooling model to extract the average representation features of the text. The results show that the overall classification accuracy of the multi-feature fusion model is 84.30%.