人物家庭网络是社会关系网络中的一个重要组成部分,因此,如何高效准确地提取出人物的家庭网络具有重要研究意义。该文在前人工作的基础上提出一种基于单文本指代消解技术的人物家庭关系抽取方法,以此扩大人物家庭关系抽取的范围,进而提高人物家庭网络的召回性能。该文还提出了一种基于人物虚拟边的家庭网络评估指标,用于更合理地评价构建出的人物家庭网络的性能。在大规模中文语料Gigaword上的实验表明,该方法可以较为准确地抽取出人物的家庭关系,进而提高人物家庭网络的召回性能,从而为社会网络分析提供基础数据。
Abstract
Personal Family Network is an important component of social networks and it is of great importance of to extract personal family relationships auto matically. This paper proposes a novel method to extend the family relation extraction via Within-Document Coreference Resolution, improving the recall of family networks constructed. Meanwhile, a new evaluation metric is devised to evaluate the performance of personal family networks more reasonably. The experimental results on a large-scale corpus of Gigaword show that, our method can extract accurate family relations while increase the recall of family networks, thereby laying the foundation for social network analysis.
关键词
社会关系网络 /
家庭网络 /
单文本指代消解
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Key words
social network /
family network /
within-document coreference resolution
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参考文献
[1] Kautz H, Selman B, Shah M. Referral Web: Combining Social Networks and Collaborative Filtering[J]. Communications of the ACM, 1997, 40(3): 63-65.
[2] Mika P. Flink: Semantic Web Technology for the Extraction and Analysis of Social Networks[J]. Web Semantics: Science, Services and Agents on the World Wide Web, 2005, 3(2): 211-223.
[3] Tang J, Zhang J, Yao L, et al. ArnetMiner: Extraction and Mining of Academic Social Networks[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2008: 990-998.
[4] Elson D K, Dames N, McKeown K R. Extracting Social Networks from Literary Fiction[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010: 138-147.
[5] Agarwal A, Corvalan A, Jensen J, et al. Social Network Analysis of Alice in Wonderland[J]. NAACL-HLT 2012, 2012: 88-96.
[6] Van De Camp M, Van Den Bosch A. A Link to the Past: Constructing Historical Social Networks[C]//Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis. Association for Computational Linguistics, 2011: 61-69.
[7] Gu J H, Hu Y N, Qian L H, et al. Research on Building Family Networks Based on Bootstrapping and Coreference Resolution[C]//Proceedings of the 2nd Natural Language Processing and Chinese Computing. Springer Berlin Heidelberg, 2013: 200-211.
[8] Zhou G D, Zhang M. Extracting Relation Information from Text Documents by Exploring Various Types of Knowledge[J]. Information Processing & Management, 2007, 43(4): 969-982.
[9] Oh J H, Uchimoto K, Torisawa K. Bilingual Co-Training for Monolingual Hyponymy-Relation Acquisition[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Association for Computational Linguistics, 2009: 432-440.
[10] Zhang M, Su J, Wang D, et al. Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering[C]//Proceedings of the 2nd International Joint Conference on Natural Language Processing(IJCNLP-2005). Springer Berlin Heidelberg, 2005: 378-389.
[11] Zhu J, Nie Z, Liu X, et al. StatSnowball: A Statistical Approach to Extracting Entity Relationships[C]//Proceedings of the 18th International Conference on World Wide Web. ACM, 2009: 101-110.
[12] Peng C, Gu J H, Qian L H. Research on Tree Kernel-Based Personal Relation Extraction[C]//Proceedings of the 1st Natural Language Processing and Chinese Computing. Springer Berlin Heidelberg, 2012: 225-236.
[13] Agarwal A, Kotalwar A, Rambow O. Automatic Extraction of Social Networks from Literary Text: A Case Study on Alice in Wonderland[C]//Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP-2013). 2013.
[14] Gordon P C, Grosz B J, Gilliom L A. Pronouns, Names, and the Centering of Attention in Discourse[J]. Cognitive Science, 1993, 17(3): 311-347.
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脚注
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基金
国家自然科学基金(61373096, 90920004);江苏省高校自然科学重大项目(11KJA520003)。
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