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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (12): 93-100    DOI: 10.11925/infotech.2096-3467.2019.0737
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Computing Text Semantic Similarity with Syntactic Network of Co-occurrence Distance
Jiao Yan1,Jing Ma1(),Kang Fang2
1 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2 Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
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Abstract  

[Objective] This paper aims to break through the limitations of existing methods for text similarity calculation by synthesizing multiple text information features such as semantics, syntax and word frequency. [Methods] First, we constructed the text complex network, combining co-occurrence distance and dependency syntax. Then, we used information entropy to determine the weights of dynamics characteristics. Finally, we utilized word embedding, syntactic structure and inverted file information to avoid the loss of word structure and semantics. [Results] Compared with the syntactic network + TF-IDF algorithm, the F1 value of the proposed algorithm increased up to 12.1%. The result was 5.8% higher than that of the co-occurrence network + semantic method. The average values of F1 were 5.8% and 1.6% better than those of the existing methods. [Limitations] The selection of relevant indicators in feature extraction needs to be further improved, which address the importance of nodes more comprehensively. [Conclusions] Compared with the traditional methods, the proposed model could reduce the loss of text information and improve the accuracy of calculating text similarity effectively.

Key wordsDependency Grammar      Text Complex Network      Semantic Similarity      Co-occurrence Distance      Feature Extraction     
Received: 24 June 2019      Published: 25 January 2020
ZTFLH:  TP391  
Corresponding Authors: Jing Ma     E-mail: majing5525@126.com

Cite this article:

Jiao Yan,Jing Ma,Kang Fang. Computing Text Semantic Similarity with Syntactic Network of Co-occurrence Distance. Data Analysis and Knowledge Discovery, 2019, 3(12): 93-100.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0737     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I12/93

source target weight source target weight
网络 费用 10 艺术 社会 8.571 429
爱好者 面对 10 艺术 描写 8.571 429
文坛 领袖 10 科技 挑战 8.571 429
网络 时代 9.285 714 口号 看待 8.571 429
诗人 词汇 9.285 714 艺术 活动 7.857 143
艺术 人类 8.571 429 艺术 主体 7.857 143
时刻 爱好 8.571 429 活动 类型 7.857 143
评价指标
次数
正确率 召回率 F1
1 89.2 88.3 88.3
2 89.8 87.5 87.4
3 89.7 89.2 89.2
4 91.2 90.8 90.8
5 90.9 90.4 90.4
6 86.8 86.3 86.3
7 80.5 80.4 80.4
8 91.6 91.3 91.3
9 93.7 93.8 93.8
10 86.3 85.8 85.8
实验
类别
本文算法 句法网络+TF-IDF 共现网络+语义
艺术 86.7 83.1 86.5
历史 74.8 62.7 73.9
计算机 93.5 95.3 93.1
环境 88.8 84.8 89.7
农业 92.8 83.9 90.6
经济 89.1 81.3 83.3
政治 88.3 80.9 85.4
体育 92.9 88.6 91.7
平均 88.4 82.6 86.8
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