Computer Science ›› 2016, Vol. 43 ›› Issue (3): 252-255.doi: 10.11896/j.issn.1002-137X.2016.03.046

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Chinese Event Argument Inference Approach Based on Markov Logic Network

ZHU Shao-hua, LI Pei-feng and ZHU Qiao-ming   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Currently,previous Chinese argument extraction approaches mainly use syntactic structure as the major features to describe the relationship between trigger and its arguments.However,they suffer much from those inter-sentence arguments which are not in the same sentence or clause of the trigger.To address this issue,this paper brought forward a novel argument inference mechanism based on the Markov logic network.It first learns the probabilities of an entity fulfilling a specific role from the training set and obtains those extracted argument mentions with high confidences in the test set.Then it uses them to extract those argument mentions with lack of effective context information or low confidences.Experimental results on the ACE 2005 Chinese corpus show that our approach outperforms the baseline significantly,with the improvements of 6.0% and 4.4% in argument identification and role determination respectively.

Key words: Argument extraction,Markov logic network,Argument inference

[1] Linguistic Data Consortium.ACE (Automatic Content Extraction) Chinese Annotation Guidelines for Events Version 5.5.1..(2009-09-08).http://www.ldc.upenn.edu/Projects/ACE
[2] Zhao Y Y,Qin B,Che W X,et al.Research on Chinese event extraction[J].Journal of Chinese Information Processing,2008,22(1):3-8(in Chinese) 赵妍妍,秦兵,车万翔,等.中文事件抽取技术研究[J].中文信息学报,2008,22(1):3-8
[3] Tan H Y.Research on Chinese event extraction[D].Harbin:Harbin Institute of Technology,2008(in Chinese) 谭红叶.中文事件抽取关键技术研究[D].哈尔滨:哈尔滨工业大学,2008
[4] Zheng Chen,Heng Ji.Language specific issue and feature exploration in Chinese event extraction[C]∥Proceeding of the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics Boulder.Colorado,USA,2009:209-212
[5] Ahn D.The Stages of Event Extraction[C]∥Proceedings of the Workshop on Annotations and Reasoning about Time and Events.2006:1-8
[6] Li Pei-feng,Zhou Guo-dong,Zhu Qiao-ming,et al.EmployingCompositional Semantics and Discourse Consistency in Chinese Event Extraction[C]∥Proc.EMNLP.2012:1006-1016
[7] Hou L B,Li P F,Zhu Q M.Study of Event Recognition Based on CRFs and Cross-event[J].Computer Engineering,2012,38(24):191-195(in Chinese) 侯立斌,李培峰,朱巧明.基于 CRFs 和跨事件的事件识别研究[J].计算机工程,2012,38(24):191-195
[8] Fu Jian-feng,Liu Zong-tian,Zhong Zhao-man,et al.Chinese Event Extraction Based on Feature Weighting[J].Information Technology Journal,2010,9(1):184-187
[9] Li Pei-feng,Zhou Guo-dong,Zhu Qiao-ming.Argument Infe-rence from Relevant Event Mentions in Chinese Argument Extraction[C]∥Proceedings of ACL.2013:1477-1487
[10] Liao Sha-sha,Grishman R.Using Document Level Cross-Event Inference to Improve Event Extraction[C]∥Porc.ACL 2010.Uppsala,Sweden,2010:789-797
[11] Hong Yu,Zhang Jian-feng,Ma Bin,et al.Using Cross-Entity Inference to Improve Event Extraction[C]∥Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics.Stroudsburg,PA,USA,2011:1127-1136
[12] Richardson M,Domingos P.Hybrid markov logic networks[J].Machine Learning,2006,62(1/2):1106-1111
[13] Poon H,Domingos P.Joint inference in information extraction[C]∥AAAI.2007:913-918
[14] Poon H,Pedro D.Joint unsupervised coreference resolution with Markov logic[C]∥Proceedings of the Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2008:650-659
[15] Poon H,Pedro D.Unsupervised semantic parsing[C]∥Procee-dings of the 2009 Conference on Empirical Methods in Natural Language Processing:Volume 1.2009:1-10
[16] Singla,Parag,Pedro D.Entity resolution with markov logic[C]∥Sixth International Conference on Data Mining,2006(ICDM’06).IEEE,2006:572-582
[17] Li Pei-feng,Zhou Guo-dong.Employing Morphological Struc-tures and Sememes for Chinese Event Extraction[C]∥COLING.2012:1619-1634

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