|Table of Contents|

Approach for topical sentence of news events extraction based on graph


Research Field:
Publishing date:


Approach for topical sentence of news events extraction based on graph
Wang YongkaiMao CunliYu ZhengtaoGuo JianyiHong XudongLuo Lin
School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
news events event topic sentences trigger word named entity event relation undirected graph ranking extraction
News events topical sentence recognition task is a text-based semantic analysis of natural language processing technology.In order to accurately calculate the news text sentences that are most relevant to the topic,this paper proposes a novel approach for topical sentence of news events extraction based on a undirected graph.This paper describes the characteristics of an event trigger Word and sentence extraction named entity and constructs the candidated event extraction templates.This paper,calculates the relationship between candidated event sentences and constructs undirected graphs of event relation ship.Finally,based on the TextRank algorithm,the weight of any vertex in the graph is represented by the weighted sum of the vertex weights,and according to the sorted weights,the event topical sentences are extracted.Experimental results show that the proposed approach is better than TFIDF and event extraction method based on title and that F values are respectively 6.26% and 2%.


[1] 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,2008,26(1):3-8.
Zhao Yanyan,Qin Bing,Che Wanxiang,et al.Research on Chinese event extraction[J].Journal of Chinese Information Processing,2008,26(1):3-8.
[2]Kim J D,Ohta T,Pyysalo S,et al.Overview of Bio NLP'09 shared task on event extraction[C]//Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing:Shared Task.Madison,USA:Omnipress,2009,32(11):77-85.
[3]Zha H.Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering[C]//Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:Association for Computing Machinery,2002:113-120.
[4]Ganesan K,Zhai C X,Han J.Opinosis:a graph-based approach to abstractive summarization of highly redundant opinions[C]//Proceedings of the 23rd International Conference on Computational Linguistics.Madison,USA:Omnipress,2010:340-348.
[5]Nishikawa H,Hasegawa T,Matsuo Y,et al.Opinion summarization with integer linear programming formulation for sentence extraction and ordering[C]//Proceedings of the 23rd International Conference on Computational Linguistics:Posters.Madison,USA:Omnipress,2010:910-918.
Lin Liyuan,Wang Zhongqing,Li Shoushan,et al.Chinese multi-docement opinion summarization via PageRank[J].Journal of Chinese Information Processing,2014,28(2):85-90.
Shi Quan,Xiao Yanghua,Lu Yiqi,et al.Top-k Subgraph Query Algorithm on UncertainSocial Networks Based on Summary Graph[J].Journal of Nanjing University of Science and Technology(NaturalScience),2014,12(6):38-743.
[8]Wang D,Liu Y.A pilot study of opinion summarization in conversations[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies-Volume 1.Madison,USA:Omnipress,2011:331-339.
[9]Boudin F.A comparison of centrality measures for graph-based keyphrase extraction[C]//International Joint Conference on Natural Language Processing(IJCNLP).Berlin,Germany:Springer,2013:834-838.
[10]Mihalcea R,Tarau P.TextRank:Bringing order into texts[C]//Proceedings of EMNLP-04 and the 2004 Conference on Empirical Methods in Natural Language Processing.Madison,USA:Omnipress,2004:404-411.
[11]Bougouin A,Boudin F,Daille B.Topicrank:Graph-based topic ranking for keyphrase extraction[C]//International Joint Conference on Natural Language Processing(IJCNLP).Berlin,Germany:Springer,2013:543-551.
[12]ACE(Automatic Content Extraction)Chinese Annotation Guidelines for Events.National Institute of Standards and Technology[R],2005.
Liu Qun,Li Sujia.Word similarity computing based on How-net[J].Computational Linguistics and Chinese Language Processing,2002,7(2):59-76.
Wang Yangyang,Liu Baisong,Liu Wei.Probe:Normalized cuts based topic partition[J].Journal of Ningbo University(NSEE),2013,26(4):40-44.


Last Update: 2016-06-30