基于灰色关联分析的中文新闻事件关联性识别

doi:10.11772/j.issn.1001-9081.2016.02.0408

计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 408-413.DOI: 10.11772/j.issn.1001-9081.2016.02.0408

• 第三届CCF大数据学术会议(CCF BigData 2015) • 上一篇下一篇

基于灰色关联分析的中文新闻事件关联性识别

刘盼盼¹, 洪旭东¹, 郭剑毅^1,2, 余正涛^1,2, 文永华^1,2, 陈玮^1,2

1. 昆明理工大学信息工程与自动化学院, 昆明 650500;
2. 昆明理工大学智能信息处理重点实验室, 昆明 650500

收稿日期:2015-08-29 修回日期:2015-09-17 出版日期:2016-02-10 发布日期:2016-02-03
通讯作者: 郭剑毅(1964-),女,河南偃师人,教授,主要研究方向:自然语言处理、信息抽取、机器学习。
作者简介:刘盼盼(1990-),女,山东济南人,硕士研究生,主要研究方向:自然语言处理、信息抽取;洪旭东(1989-),男,安徽马鞍山人,博士研究生,主要研究方向:自然语言处理、信息检索;余正涛(1970-),男,云南曲靖人,教授,博士,主要研究方向:自然语言处理、信息检索、机器学习、数据挖掘;文永华(1979-),男,云南大理人,博士研究生,主要研究方向:自然语言处理、信息抽取;陈玮(1983-),男,云南曲靖人,博士研究生,主要研究方向:信息检索、机器学习、信息抽取。
基金资助:
国家自然科学基金资助项目(61262041,61472168,61562052);云南省自然科学基金重点资助项目(2013FA030)。

Recognition of Chinese news event correlation based on grey relational analysis

LIU Panpan¹, HONG Xudong¹, GUO Jianyi^1,2, YU Zhengtao^1,2, WEN Yonghua^1,2, CHEN Wei^1,2

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650500, China;
2. Intelligent Information Processing Key Laboratory, Kunming University of Science and Technology, Kunming Yunnan 650500, China

Received:2015-08-29 Revised:2015-09-17 Online:2016-02-10 Published:2016-02-03

摘要/Abstract

摘要： 针对中文新闻事件关联性识别准确率较低的问题,提出一种基于灰色关联分析(GRA)的中文新闻事件关联性识别算法,该算法是一种多因素分析法。首先,通过分析中文新闻事件的特性,提出三个影响事件关联性的因素,分别为触发词的共现性、事件的共享名词以及事件句的相似度;其次,对多个影响因素进行量化处理,计算每个影响因素的影响权值;最后,运用GRA将多个影响因素结合在一起,建立事件之间的灰色关联性分析模型,实现事件关联性识别。通过实验验证了三个影响因素对事件关联性识别的有效性,而且相对于只考虑单一影响因素的关联性识别算法,所提算法提高了事件关联性识别的准确率。

关键词: 事件关联性识别, 灰色关联分析, 多因素分析法, 共现性, 共享名词, 相似度

Abstract: Concerning the low accuracy of identifying relevant Chinese events, a correlation recognition algorithm for Chinese news events based on Grey Relational Analysis (GRA) was proposed, which is a multiple factor analysis method. Firstly, three factors that affect the event correlation, including co-occurrence of triggers, shared nouns between events and the similarity of the event sentences, were proposed through analyzing the characteristics of Chinese news events. Secondly, the three factors were quantified and the influence weights of them were calculated. Finally, GRA was used to combine the three factors, and the GRA model between events was established to realize event correlation recognition. The experimental results show that the three factors for event correlation recognition are effective, and compared with the method only using one influence factor, the proposed algorithm improves the accuracy of event correlation recognition.

Key words: event correlation recognition, Grey Relational Analysis(GRA), multi-factor analysis method, co-occurrence, shared noun, similarity

中图分类号:

TP181

刘盼盼, 洪旭东, 郭剑毅, 余正涛, 文永华, 陈玮. 基于灰色关联分析的中文新闻事件关联性识别[J]. 计算机应用, 2016, 36(2): 408-413.

LIU Panpan, HONG Xudong, GUO Jianyi, YU Zhengtao, WEN Yonghua, CHEN Wei. Recognition of Chinese news event correlation based on grey relational analysis[J]. Journal of Computer Applications, 2016, 36(2): 408-413.

参考文献

[1] 马彬,洪宇,杨雪蓉,等.基于语义依存线索的事件关系识别方法研究[J].北京大学学报(自然科学版),2013, 49(1):109-116. (MA B, HONG Y, YANG X R, et al. Using event dependency cue inference to recognize event relation[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2013, 49(1): 109-116.)
[2] CHKLOVSKI T, PANTEL P. VerbOcean: mining the Web for fine-grained semantic verb relations[C]//EMNLP '04: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2004: 33-40.
[3] TORISAWA K. Acquiring inference rules with temporal constraints by using Japanese coordinated sentences and noun-verb co-occurrences[C]//HLT-NAACL '06: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2006: 57-64.
[4] ZANZOTTO F M, PENNACCHIOTTI M, PAZIENZA M T. Discovering asymmetric entailment relations between verbs using selectional preferences[C]//ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2006: 849-856.
[5] PANTEL P, PENNACCHIOTTI M. Espresso: leveraging generic patterns for automatically harvesting semantic relations[C]//ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2006: 113-120.
[6] ABE S, INUI K, MATSUMOTO Y. Two-phased event relation acquisition: coupling the relation-oriented and argument-oriented approaches[C]//COLING '08: Proceedings of the 22nd International Conference on Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2008, 1: 1-8.
[7] LIN D, PANTEL P. Discovery of inference rules from text[C]//KDD '01: Proceeding of the 7th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. New York: ACM, 2001: 323-328.
[8] SZPEKTOR I, TANEV H, DAGAN I, et al. Scaling Web-based acquisition of entailment relations[C]//EMNLP '04: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2004: 41-48.
[9] PEKAR V. Acquisition of verb entailment from text[C]//HLT-NAACL '06: Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2006: 49-56.
[10] BOGURAEV B, ANDO R K. TimeML-compliant text analysis for temporal reasoning[C]//IJCAI '05: Proceedings of the 19th International Joint Conference on Artificial intelligence. San Francisco, CA: Morgan Kaufmann, 2005: 997-1003.
[11] MANI I, VERHAGEN M, WELLNER B, et al. Machine learning of temporal relations[C]//ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2006: 753-760.
[12] CHAMBERS N, WANG S, JURAFSKY D. Classifying temporal relations between events[C]//ACL '07: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. Stroudsburg, PA: Association for Computational Linguistics, 2007: 173-176.
[13] TATU M, SRIKANTH M. Experiments with reasoning for temporal relations between events[C]//COLING '08: Proceedings of the 22nd International Conference on Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2008, 1: 857-864.
[14] LI W J, WONG K-F, CAO G H, et al. Applying machine learning to Chinese temporal relation resolution[C]//ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2004: Article No. 582.
[15] CHENG Y C, ASAHARA M, MATSUMOTO Y. Use of event types for temporal relation identification in Chinese text[C]//Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2008: 31-38.
[16] DING X S, LI F, ZHANG D M. Causal relation recognition between sentence-based events[C]//Proceedings of the 2011 Chinese Control and Decision Conference. Piscataway, NJ: IEEE, 2011: 1688-1693.
[17] SHIBATA T, KUROHASHI S. Acquiring strongly-related events using predicate-argument co-occurring statistics and case frames[C]//IJCNLP '11: Proceedings of the 5th International Joint Conference on Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2011: 1028-1036.
[18] 杨雪蓉,洪宇,马彬,等.基于核心词和实体推理的事件关系识别方法[J].中文信息学报,2014,28(2):100-108. (YANG X R, HONG Y, MA B, et al. Event relation recognition by event term and entity inference[J]. Journal of Chinese Information Processing, 2014, 28(2): 100-108.)
[19] 荣莉莉,蔡莹莹,王铎.基于共现分析的我国突发事件关联研究[J].系统工程,2011,29(6):1-7. (RONG L L, CAI Y Y, WANG D. Relations of emergency events occurring in China based on the co-occurrence analysis[J]. Systems Engineering, 2011, 29(6): 1-7.)
[20] 张辉,李国辉,徐新文,等.词网络的新闻事件关联建模[J].国防科技大学学报,2014,36(4):169-176. (ZHANG H, LI G H, XU X W, et al. Modeling association of news events on term network[J]. Journal of National University of Defense Technology, 2014, 36(4): 169-176.)
[21] WEI G W. Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information[J]. Expert Systems with Applications, 2011, 38(5): 4824-4828.
[22] WEI G W. Grey relational analysis model for dynamic hybrid multiple attribute decision making[J]. Knowledge-based Systems, 2011, 24(5): 672-679.
[23] 邓聚龙.灰色系统基本方法[M].武汉:华中科技大学出版社,2005:13-15. (DENG J L. The primary methods of grey system theory[M]. Wuhuan: Huazhong University of Science and Technology Press, 2005: 13-15.)
[24] 罗均,王琦.产品设计质量灰色系统综合评价方法的研究[J].机械科学与技术,2000,19(5):747-749. (LUO J, WANG Q. A study on comprehensive evaluation for products design quality based on grey system theory[J]. Mechanical Science and Technology, 2000, 19(5): 747-749.)
[25] CHANG K-C, YEH M-F. Grey relational analysis based approach for data clustering[J]. IEE Proceedings — Vision, Image & Signal Processing, 2005, 152(2): 165-172.
[26] 苏博,刘鲁,杨方廷.基于灰色关联分析的神经网络模型[J].系统工程理论与实践,2008,28(9):98-104. (SU B, LIU L, YANG F T. Research of artificial neural network forecasting model based on grey relational analysis[J]. Systems Engineering — Theory & Practice, 2008, 28(9): 98-104.)
[27] 鲁峰,黄金泉.基于灰色关联聚类的特征提取算法[J].系统工程理论与实践,2012,32(4):872-876. (LU F, HUANG J Q. Feature extraction algorithm of clustering based on grey relational theory[J]. Systems Engineering — Theory & Practice, 2012, 32(4): 872-876.)
[28] 张文泉,赵凯,张贵彬,等.基于灰色关联度分析理论的底板破坏深度预测[J].煤炭学报,2015,40(S1):53-59. (ZHANG W Q, ZHAO K, ZHANG G B, et al. Prediction of floor failure depth based on grey correlation analysis theory[J].Journal of China Coal Society, 2015, 40(S1): 53-59.)
[29] 仲兆满,刘宗田,周文,等.事件关系表示模型[J].中文信息学报,2009,23(6):56-60. (ZHONG Z M, LIU Z T, ZHOU W, et al. The model of event relation representation[J]. Journal of Chinese Information Processing, 2009, 23(6): 56-60.)
[30] 张牧宇,宋原,秦兵,等.中文篇章级句间语义关系识别[J].中文信息学报,2013,27(6):51-57. (ZHANG M Y, SONG Y, QIN B, et al. Chinese discourse relation recognition[J]. Journal of Chinese Information Processing, 2013, 27(6): 51-57.)
[31] BARZILAY R, LAPATA M. Modeling local coherence: an entity-based approach[C]//ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2005: 141-148.
[32] GUINAUDEAU C, STRUBE M. Graph-based local coherence modeling[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2013, 1: 93-103.
[33] 刘思峰,蔡华,杨英杰,等.灰色关联分析模型研究进展[J].系统工程理论与实践,2013,33(8): 2041-2046. (LIU S F, CAI H, YANG Y J, et al. Advance in grey incidence analysis modelling[J]. Systems Engineering——Theory & Practice, 2013, 33(8): 2041-2046.)

基于灰色关联分析的中文新闻事件关联性识别

Recognition of Chinese news event correlation based on grey relational analysis

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