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

计量
- 文章访问数: 1773
- HTML全文浏览量: 57
- PDF下载量: 915
- 被引次数: 0
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

HTML全文

参考文献(36)

[1]	Zhou M, Ding Y, Huang C N. Improving translation selection with a new translation model trained by independent monolingual corpora. Computational Linguistics and Chinese Language Processing, 2001, 6(1): 1-26
[2]	[2] Leacock C, Chodorow M. Combining Local Context and WordNet Similarity for Word Sense Identification. Cambridge: MIT Press, 1998. 265-283
[3]	Lu Wen-Peng, Huang He-Yan, Wu Hao. Word sense disambiguation with graph model based on domain knowledge. Acta Automatica Sinica, 2006, 40(12): 2836-2850(鹿文鹏, 黄河燕, 吴昊. 基于领域知识的图模型词义消歧方法. 自动化学报, 2014, 40(12): 2836-2850)
[4]	Liu Yu-Peng, Li Sheng, Zhao Tie-Jun. System combination based on WSD using wordnet. Acta Automatica Sinica, 2010, 36(11): 1575-1580(刘宇鹏, 李生, 赵铁军. 基于WordNet词义消歧的系统融合. 自动化学报, 2010, 36(11): 1575-1580)
[5]	[5] Hassan H, Hassan A, Emam O. Unsupervised information extraction approach using graph mutual reinforcement. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2006. 501-508
[6]	Li Wen-Qing, Sun Xin, Zhang Chang-You, Feng Ye. A semantic similarity measure between ontological concepts. Acta Automatica Sinica, 2012, 38(2): 229-235(李文清, 孙新, 张常有, 冯烨. 一种本体概念的语义相似度计算方法. 自动化学报, 2012, 38(2): 229-235)
[7]	[7] Cui Q, Gao B, Bian J, Qiu S, Liu T Y. KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge. arXiv: 1407.1687, 2014. 1-16
[8]	[8] Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(1): 17-30
[9]	[9] Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1995. 448-453
[10]	Wu Z B, Palmer M. Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 1994. 133-138
[11]	Agirre E, Rigau G. A proposal for word sense disambiguation using conceptual distance. In: Proceedings of the 1st International Conference on Recent Advances in Natural Language Processing. Stroudsburg, Cambridge: MIT Press, 1995. 35-43
[12]	Jiang J J, Conrath D W. Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the 1997 International Conference on Research in Computational Linguistics. Stroudsburg, PA: ACL, 1997. 19-33
[13]	Lin D K. An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1998. 296-304
[14]	Hirst G, St-Onge D. Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms. Cambridge: MIT Press, 1998. 305-332
[15]	Li Y H, Bandar Z A, McLean D. An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(4): 871-882
[16]	Yang D Q, Powers D M W. Measuring semantic similarity in the taxonomy of wordnet. In: Proceedings of the 28th Australasian Conference on Computer Science. Darlinghurst, Australia, Australia: Australian Computer Society, Inc., 2005. 315-322
[17]	Budanitsky A, Hirst G. Evaluating wordnet-based measures of lexical semantic relatedness. Computational Linguistics, 2006, 32(1): 13-47
[18]	Alvarez M A, Lim S J. A graph modeling of semantic similarity between words. In: Proceedings of the 2007 International Conference on Semantic Computing. Irvine, CA: IEEE, 2007. 355-362
[19]	Qin P, Lu Z, Yan Y, Wu F. A new measure of word semantic similarity based on wordnet hierarchy and DAG theory. In: Proceedings of the 2009 International Conference on Web Information Systems and Mining. Shanghai, China: IEEE, 2009. 181-185
[20]	Pirr G. A semantic similarity metric combining features and intrinsic information content. Data Knowledge Engineering, 2009, 68(11): 1289-1308
[21]	Cai S M, Lu Z. An improved semantic similarity measure for word pairs. In: Proceedings of 2010 International Conference on e-Education, e-Business, e-Management and e-Learning. Sanya, China: IEEE, 2010. 212-216
[22]	Snchez D, Batet M, Isern D. Ontology-based information content computation. Knowledge-Based Systems, 2011, 24(2): 297-303
[23]	Snchez D, Batet M, Isern D, Valls A. Ontology-based semantic similarity: a new feature-based approach. Expert Systems with Applications, 2012, 39(9): 7718-7728
[24]	Liu H Z, Bao H, Xu D. Concept Vector for semantic similarity and relatedness based on WordNet structure. Journal of Systems and Software, 2012, 85(2): 370-381
[25]	Dagan I, Lee L, Pereira F C N. Similarity-based models of word cooccurrence probabilities. Machine Learning, 1999, 34(1-3): 43-69
[26]	Brown P F, Pietra S A D, Pietra V J D, Mercer R L. Word-sense disambiguation using statistical methods. In: Proceedings of the 29th Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 1991. 264-270
[27]	Lee L. Similarity-based Approaches to Natural Language Processing [Ph.D. dissertation], Harvard University, Cambridge, MA, USA, 1997.
[28]	Liu L, Zhong M S, Lu R Z. Measuring word similarity based on pattern vector space model. In: Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence. Piscataway, NJ: IEEE, 2009. 72-76
[29]	Xu T, Qu W G, Tang X R, Ding D X, Li B, Li H. Computing word similarity on large-scale corpus. In: Proceedings of the 4th International Conference on Innovative Computing, Information and Control. Kaohsiung: IEEE, 2009. 1076-1079
[30]	Radinsky K, Agichtein E, Gabrilovich E, Markovitch S. A word at a time: computing word relatedness using temporal semantic analysis. In: Proceedings of the 20th international conference on World Wide Web. New York, NY, USA: ACM, 2011. 337-346
[31]	Shafer G. A Mathematical Theory of Evidence. Princeton: Princeton University Press, 1976.
[32]	Rubenstein H, Goodenough J B. Contextual correlates of synonymy. Communications of the ACM, 1965, 8(10): 627-633
[33]	Zhou Hao, Li Shao-Hong. New combination algorithm of conflict evidences introduced by GDOP. Control and Decision, 2010, 25(2): 278-281(周皓, 李少洪. GDOP引出的冲突证据组合新算法. 控制与决策, 2010, 25(2): 278-281)
[34]	Voorbraak F. A Computationally efficient approximation of Dempster-Shafer theory. International Journal of Man-Machine Studies, 1989, 30(5): 525-536
[35]	Miller G, Charles W. Contextual correlates of semantic similarity. Language and Cognitive Processes, 1991, 6(1): 1-28
[36]	Finkelstein L, Gabrilovich E, Matias Y, Rivlin E, Solan Z, Wolfman G, Ruppin E. Placing search in context: the concept revisited. ACM Transactions on Information Systems, 2002, 20(1): 116-131

施引文献

资源附件(0)

访问统计

计量

文章访问数: 1773
HTML全文浏览量: 57
PDF下载量: 915
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

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

Word Semantic Similarity Measurement Based on Evidence Theory

计量

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