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数据分析与知识发现  2019, Vol. 3 Issue (12): 93-100     https://doi.org/10.11925/infotech.2096-3467.2019.0737
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于融合共现距离的句法网络下文本语义相似度计算 *
严娇1,马静1(),房康2
1 南京航空航天大学经济与管理学院 南京 211106
2 南京大学计算机科学与技术系 南京 210023
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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摘要 

【目的】综合语义、句法和词频等多种文本信息特征, 突破现有文本相似度计算的局限。【方法】构建融合共现距离和依存句法的文本复杂网络, 运用信息熵确定网络动力学特征指标的权重。利用词嵌入、句法结构和倒排档信息避免词语结构和语义的缺失。【结果】对比实验结果表明, 不同类别下本文算法分类效果的F1值较句法网络+TF-IDF方法最高提高12.1%, 比共现网络+语义方法最高提高5.8%。本文算法的各类别分类效果的平均F1值较二者分别提高5.8%和1.6%。【局限】特征提取中对各指标的选取有待改进, 以更全面地区分节点间的重要性。【结论】与传统方法相比, 本文算法减少了文本信息流失并实现文本降维, 有效地提高了文本相似度计算的准确率。

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严娇
马静
房康
关键词 依存句法文本复杂网络语义相似度共现距离特征提取    
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
收稿日期: 2019-06-24      出版日期: 2019-12-25
ZTFLH:  TP391  
基金资助:*本文系国家自然科学基金项目“基于演化本体的网络舆情自适应话题跟踪方法研究”(项目编号: 71373123);中央高校基本科研业务费专项前瞻性发展策略研究资助项目“基于大数据技术的跨境电商政府管理范式研究”(项目编号: NW2018004)
通讯作者: 马静     E-mail: majing5525@126.com
引用本文:   
严娇,马静,房康. 基于融合共现距离的句法网络下文本语义相似度计算 *[J]. 数据分析与知识发现, 2019, 3(12): 93-100.
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0737      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I12/93
  依存句法分析结果
  融合共现距离的句法网络
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
  句法网络中部分节点对及边权
  参数$\alpha $对相似度分类效果的影响
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
  十折交叉验证每次结果的各评价指标值(%)
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
  不同类别的三组实验结果F1值(%)
  不同类别的三组实验结果F1
  三组实验实验结果的平均F1
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