Improved lexical semantic tendentiousness recognition computing

doi:10.3778/j.issn.1002-8331.2009.21.026

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (21): 91-93.DOI: 10.3778/j.issn.1002-8331.2009.21.026

• 数据库、信息处理 • Previous Articles Next Articles

Improved lexical semantic tendentiousness recognition computing

YANG Yu-bing，WU Xian-wei

Electronic Information Branch，Ningbo Dahongying College，Ningbo，Zhejiang 315175，China

Received:2009-03-03 Revised:2009-04-29 Online:2009-07-21 Published:2009-07-21
Contact: YANG Yu-bing

改进的基于知网词汇语义褒贬倾向性计算

杨昱昺，吴贤伟

宁波大红鹰学院电子信息学院，浙江宁波 315175

通讯作者: 杨昱昺

Abstract

Abstract: Lexical semantic tendentiousness recognition computing is the base of the sentence tendentiousness，and the sentence tendentiousness recognition is the text tendentiousness recognition and the chapter structure tendentiousness recognition foundation.Based on HowNet lexical semantic similarity computing，according to the current vocabulary appraise tendentious theory used by the method of computing similarity between words and benchmark words，from appraise benchmark words and computing formula，the paper puts forward an improved method.With experiment validation，in the same pair of benchmark words，accuracy rate has greatly been improved，arriving at 98.94%，there is some value in practical application.

Key words: semantic similarity, tendentiousness recognition, HowNet, appraise benchmark words

摘要： 词汇语义褒贬倾向性研究是句子褒贬倾向性识别的基础，而句子褒贬倾向性识别又是文本倾向性识别和篇章结构褒贬倾向性识别的基础。以《知网》的词汇语义相似度计算为基础，针对目前采用计算基准词对与词汇相似度的方法识别词汇褒贬倾向性理论，从褒贬基准词和计算公式入手，提出了改进办法。实验证明，在同样基准词对下，准确率得到了很大的提高，达到98.94%，具有实际应用价值。

关键词: 语义相似度, 倾向性识别, 知网, 褒贬基准词

YANG Yu-bing，WU Xian-wei. Improved lexical semantic tendentiousness recognition computing[J]. Computer Engineering and Applications, 2009, 45(21): 91-93.

杨昱昺，吴贤伟. 改进的基于知网词汇语义褒贬倾向性计算[J]. 计算机工程与应用, 2009, 45(21): 91-93.

[1]	SHI Chen, ZHANG Yu, HU Bo. Model for Near-Synonym/Synonym Phrase Finding Based on Common Surrounding Context [J]. Computer Engineering and Applications, 2021, 57(14): 142-147.
[2]	QIAO Weitao, HUANG Haiyan, WANG Shan. Semantic Similarity Calculation Based on Transformer Encoder [J]. Computer Engineering and Applications, 2021, 57(14): 158-163.
[3]	YUAN Zhongchen, MA Zongmin. Ensemble Classification for UML Class Diagram Based on Semantics [J]. Computer Engineering and Applications, 2021, 57(12): 257-262.
[4]	XU Ge, YANG Xiaoyan, WANG Tao. Survey on Semantic Similarity Calculation of Words [J]. Computer Engineering and Applications, 2020, 56(4): 9-15.
[5]	YAO Jiaqi, XU Zhengguo, YAN Jikun, XIONG Gang, LI Zhixiang. Dynamic Multi-label Text Classification Algorithm Based on Label Semantic Similarity [J]. Computer Engineering and Applications, 2020, 56(19): 94-98.
[6]	YANG Quan, SUN Yuquan. Research on Semantic Similarity Calculation Based on Depth of CiLin [J]. Computer Engineering and Applications, 2020, 56(17): 48-54.
[7]	GAO Yongbing1, ZHANG Guijuan1, HU Wenjiang1, MA Zhanfei2. Research of regional microblog summarization based on Suffix Tree Clustering algorithm [J]. Computer Engineering and Applications, 2018, 54(9): 126-132.
[8]	HAN Xueren1, WANG Qingshan1, GUO Yong1, CUI Xingya2. Geographic ontology concept semantic similarity measure model based on BP neural network optimized by PSO [J]. Computer Engineering and Applications, 2017, 53(8): 32-37.
[9]	CHEN Hongyang, WANG Linlin, LU Jiangkun, TANG Zhi, WANG Feixue. Research on method of topic tracking for micro-blog texts based on double topic model [J]. Computer Engineering and Applications, 2017, 53(16): 144-148.
[10]	QIU Yunfei, ZHAO Bin, LIN Mingming, WANG Wei. Improved K-means clustering algorithm combined semantic similarity of short text [J]. Computer Engineering and Applications, 2016, 52(19): 78-83.
[11]	ZHANG Sen, CAO Hui. Research on building Chinese semantic lexicon based on concept definition of HowNet [J]. Computer Engineering and Applications, 2015, 51(17): 118-123.
[12]	OUYANG Liubo, TAN Ruizhe. Query expansion method based on ontology and user log [J]. Computer Engineering and Applications, 2015, 51(1): 151-155.
[13]	DONG Lili1, LI Huan1, ZHANG Xiang1, LIU Yanfeng2. Method for automatic extraction of Chinese domain concepts [J]. Computer Engineering and Applications, 2014, 50(6): 127-131.
[14]	ZHAN Zhijian, YANG Xiaoping. Text similarity calculation based on language network and semantic information [J]. Computer Engineering and Applications, 2014, 50(5): 33-38.
[15]	YU Hongzhi, XIA Jianhua, WAN Fucheng, CHEN Xinyi. Automatic scoring algorithm for Tibetan subjective questions based on multi-features combination [J]. Computer Engineering and Applications, 2014, 50(5): 216-220.

Improved lexical semantic tendentiousness recognition computing

改进的基于知网词汇语义褒贬倾向性计算

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics