基于证据理论的单词语义相似度度量

王俊华; 左祥麟; 左万利

doi:10.16383/j.aas.2015.c131141

基于证据理论的单词语义相似度度量

doi: 10.16383/j.aas.2015.c131141

王俊华^1,2,3,
左祥麟^1,2,
左万利^1,2, ,

1.
吉林大学计算机科学与技术学院长春 130012;
2.
符号计算与知识工程教育部重点实验室(吉林大学) 长春 130012;
3.
长春工业大学计算机科学与工程学院长春 130012

基金项目:

国家自然科学基金(60903098, 60973040, 61300148, 61472049), 吉林省重点科技攻关项目(20130206051GX),吉林省科技计划青年基金项目(20130522112JH)资助

详细信息

作者简介:
王俊华吉林大学计算机科学与技术学院博士研究生. 2005 年获得东北师范大学传媒科学学院学士学位. 主要研究方向为自然语言处理与Web 数据挖掘.E-mail: wangjunhua1982@126.com

通讯作者:
左万利吉林大学计算机科学与技术学院教授. 1982 年获得吉林大学计算机科学与技术学院学士学位. 主要研究方向为信息检索, 自然语言处理, 本体工程与Web 数据挖掘. E-mail: zuowl@jlu.edu.cn

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- 文章访问数: 1758
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出版历程
- 收稿日期: 2013-12-13
- 修回日期: 2014-10-27
- 刊出日期: 2015-06-20

Word Semantic Similarity Measurement Based on Evidence Theory

WANG Jun-Hua^1,2,3,
ZUO Xiang-Lin^1,2,
ZUO Wan-Li^{1,2
, ,}

1.
College of Computer Science and Technology, Jilin University, Changchun 130012;
2.
Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012;
3.
School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012

Funds:

Supported by National Natural Science Foundation of China (60903098, 60973040, 61300148, 61472049), Key Scientific and Technological Break-through Program of Jilin Province (20130206051GX), and Science and Technology Planning Youth Fund Project of Jilin Province (20130522112JH)

摘要

摘要: 单词语义相似度度量一直是自然语言处理领域的经典和热点问题, 其成果可对词义消歧、机器翻译、本体映射、计算语言学等应用具有重要影响. 本文通过结合证据理论和知识库,提出一个新颖的度量单词语义相似度度量途径. 首先,借助通用本体WordNet获取证据;其次,利用散点图分析证据的合理性; 然后,使用统计和分段线性插值生成基本信任分配函数;最后,结合证据冲突处理、重要度分配和D-S合成规则实现信息融合获得全局基本信任分配函数, 并在此基础上量化单词语义相似度.在数据集RG(65)上, 对比本文算法评判结果与人类评判结果的相关度,采用5折交叉验证对算法进行分析, 相关度达到0.912,比当前最优方法PS高出0.4个百分点, 比经典算法reLHS、distJC、simLC、simL和simR高出7%～13%; 在数据集MC(30)和WordSim353上也取得了比较好的实验结果, 相关度分别为0.915和0.941;且算法的运行效率和经典算法相当. 实验结果显示使用证据理论解决单词语义相似度问题是合理有效的.
- 词计算 /
- 统计学习 /
- 证据理论 /
- 不确定性度量
Abstract: Measuring semantic similarity between words is a classical and hot problem in nature language processing, the achievement of which has great impact on many applications such as word sense disambiguation, machine translation, ontology mapping, computational linguistics, etc. This paper proposes a novel approach to measure words semantic similarity by combining evidence theory with knowledge base. Firstly, we extract evidences based on WordNet; secondly, we analyze the reasonableness of the extracted evidence using scatter plot; thirdly, we generate basic probability assignment by statistics and piecewise linear interpolation technique; fourthly, we obtain global basic probability assignment by integrating evidence conflict resolution, importance distribution, and D-S combination rules; finally, we quantify word semantic similarity. On data set RG(65), we conducted experiment through 5-fold cross validation, and the correlation of our experimental results with human judgment was 0.912, with 0.4% improvements over existing best practice PS, 7%～13% improvements over classical methods (reLHS、distJC、simLC、simL, simR); the experimental results based on MC(30) and WordSim353 were also good with correlations being 0.915 and 0.941. The operational efficiency of our method is as good as classical methods', showing that using evidence theory to measure word semantic similarity is reasonable and effective.
- Computing with word /
- statistical learning /
- evidence theory /
- uncertainty modeling

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参考文献(36)

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基于证据理论的单词语义相似度度量

doi: 10.16383/j.aas.2015.c131141

作者简介:
王俊华吉林大学计算机科学与技术学院博士研究生. 2005 年获得东北师范大学传媒科学学院学士学位. 主要研究方向为自然语言处理与Web 数据挖掘.E-mail: wangjunhua1982@126.com

通讯作者:
左万利吉林大学计算机科学与技术学院教授. 1982 年获得吉林大学计算机科学与技术学院学士学位. 主要研究方向为信息检索, 自然语言处理, 本体工程与Web 数据挖掘. E-mail: zuowl@jlu.edu.cn

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Word Semantic Similarity Measurement Based on Evidence Theory

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目录

留言板

基于证据理论的单词语义相似度度量

doi: 10.16383/j.aas.2015.c131141

作者简介: 王俊华 吉林大学计算机科学与技术学院博士研究生. 2005 年获得东北师范大学传媒科学学院学士学位. 主要研究方向为自然语言处理与Web 数据挖掘.E-mail: wangjunhua1982@126.com

通讯作者: 左万利 吉林大学计算机科学与技术学院教授. 1982 年获得吉林大学计算机科学与技术学院学士学位. 主要研究方向为信息检索, 自然语言处理, 本体工程与Web 数据挖掘. E-mail: zuowl@jlu.edu.cn

计量

出版历程

Word Semantic Similarity Measurement Based on Evidence Theory

计量

出版历程

目录

作者简介:
王俊华吉林大学计算机科学与技术学院博士研究生. 2005 年获得东北师范大学传媒科学学院学士学位. 主要研究方向为自然语言处理与Web 数据挖掘.E-mail: wangjunhua1982@126.com

通讯作者:
左万利吉林大学计算机科学与技术学院教授. 1982 年获得吉林大学计算机科学与技术学院学士学位. 主要研究方向为信息检索, 自然语言处理, 本体工程与Web 数据挖掘. E-mail: zuowl@jlu.edu.cn