CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification.
CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification.
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CharAs-CBert：字符辅助构建-Bert句子表示改善情感分类
DOI：
10.3390/s22135024
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发表时间：
2022-07-03
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影响因子：
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In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sentence representation method for character-assisted construction-Bert (CharAs-CBert) to improve the accuracy of sentiment text classification. First, based on the construction, a more effective construction vector is generated to distinguish the basic morphology of the sentence and reduce the ambiguity of the same word in different sentences. At the same time, it aims to strengthen the representation of salient words and effectively capture contextual semantics. Second, character feature vectors are introduced to explore the internal structure information of sentences and improve the representation ability of local and global semantics. Then, to make the sentence representation have better stability and robustness, character information, word information, and construction vectors are combined and used together for sentence representation. Finally, the evaluation and verification are carried out on various open-source baseline data such as ACL-14 and SemEval 2014 to demonstrate the validity and reliability of sentence representation, namely, the F1 and ACC are 87.54% and 92.88% on ACL14, respectively.
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影响因子：
8.8
作者：
Zhao, Di;Wang, Jian;Yang, Zhihao
通讯作者：
Yang, Zhihao
影响因子：
3.9
作者：
Seo, Jaejin;Lee, Sangwon;Choi, Wonik
通讯作者：
Choi, Wonik
使用多视图注意力模型学习情感句子表示
影响因子：
8.1
作者：
Zhang, You;Wang, Jin;Zhang, Xuejie
通讯作者：
Zhang, Xuejie
影响因子：
2.6
作者：
Fu, Qunchao;Wang, Cong;Han, Xu
通讯作者：
Han, Xu
影响因子：
6
作者：
Rao, Guozheng;Huang, Weihang;Cong, Qiong
通讯作者：
Cong, Qiong