Computer Science ›› 2014, Vol. 41 ›› Issue (2): 270-275.

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Research on Information Extration Model for Microblog Content

ZHENG Ying and LI Da-hui   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Social media is the platform or tool that people use to share opinions,insights,ideas and experience.It has become the new media having great influence.Microblogging is an important part of social media,so it will play an important role in the information transfer.Microblogged content-oriented information extraction is to extract the valuable structred information from free text of full of noise,loose,unstructured microblogging content to facilitate effective access to information from Twitter content.This paper proposed a microblogging event extraction based on factor graph approach to accurately extract the events reflected in microblogging.At last we used some experiments to verify the effectiveness of the methods,and the results show that the performance and accuracy of this method is higher than other methods.

Key words: Social media,Microblog,Event extraction,Factor graph

[1] Wikipedia.Facebook user statistics.http://en.wikipe-dia.org/wiki/Facebook,2013
[2] Wikipedia.Twitter user statistics.http://en.wikipedia.org/wiki/twitter,2013
[3] How many Twitter Users Are There 2012.http://www.howmanyarethere.org/how-many-twitter-users-are-there-2012/2/,2013
[4] How Many Facebook Users Are There.http://www.howmanyarethere.org/how-many-facebook-users-are-there-2012/,2013
[5] Settles B.Biomedical named entity recognition using conditional random fields and rich feature sets[C]∥Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications.Association for Computational Linguistics,2004:104-107
[6] Xiao J,Su J,Zhou G,et al.Protein-protein interaction extrac-tion:a supervised learning approach[C]∥Proc Symp on Semantic Mining in Biomedicine.2005:51-59
[7] Richardson M,Domingos P.Markov logic networks [J].Ma-chine learning,2006,62(1/2):107-136
[8] Casella G,George E I.Explaining the Gibbs sampler [J].TheAmerican Statistician,1992,46(3):167-174
[9] McClosky D,Charniak E,Johnson M.Effective self-training for parsing[C]∥Proceedings of the main conference on human language technology conference of the North American Chapter of the Association of Computational Linguistics.Association for Computational Linguistics,2006:152-159
[10] Yates A,Cafarella M,Banko M,et al.TextRunner:open information extraction on the Web[C]∥Proceedings of Human Language Technologies:The Annual Conference of the North American Chapter of the Association for Computational Linguistics:Demonstrations.Association for Computational Linguistics,2007:25-26
[11] Grishman R,Westbrook D,Meyers A.NYU’s English ACE2005system description[C]∥Proc.ACE 2005Evaluation Workshop.2005
[12] Liao S,Grishman R.Using document level cross-event inference to improve event extraction[C]∥Proceedings of the 48th AnnualMeeting of the Association for Computational Linguistics.Association for Computational Linguistics,2010:789-797
[13] Bethard S,Martin J H.Identification of event mentions and their semantic class[C]∥Proceedings of the 2006Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2006:146-154
[14] Llorens H,Saquete E,Navarro-Colorado B.TimeML events re-cognition and classification:learning CRF models with semantic roles[C]∥Proceedings of the 23rd International Conference on Computational Linguistics.Association for Computational Linguistics,2010:725-733
[15] Yu L C,Chan C L,Lin C C,et al.Mining association language patterns using a distributional semantic model for negative life event classification [J].Journal of biomedical informatics,2011,44(4):509-518
[16] Sankaranarayanan J,Samet H,Teitler B E,et al.TwitterStand:news in tweets[C]∥SIGSPATIAL,GIS’09.New York,NY,USA:ACM Press,2009:42-51
[17] Sakaki T,Okazaki M,Matsuo Y.Earthquake shakes Twitter u-sers:real-time event detection by social sensors[C]∥WWW’10.2010:851-860
[18] Benson E,Haghighi A,Barzilay R.Event discovery in socialmedia feeds[C]∥Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies-Volume 1.Association for Computational Linguistics,2011:389-398
[19] Agarwal A,Rambow O.Automatic detection and classification of social events[C]∥Proceedings of the 2010Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2010:1024-1034
[20] Ritter A,Clark S,Etzioni O.Named entity recognition intweets:an experimental study[C]∥Proceedings of the Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2011:1524-1534
[21] Murphy K P,Weiss Y,Jordan M I.Loopy belief propagation for approximate inference:An empirical study[C]∥Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence.Morgan Kaufmann Publishers Inc.,1999:467-475
[22] Fletcher R.Practical methods of optimization [M].1987
[23] Nocedal J.Updating quasi-Newton matrices with limited storage [J].Mathematics of computation,1980,35(151):773-782
[24] 杨武,宋静静,唐继强.中文微博情感分析中主客观句分类方法[J].重庆理工大学学报:自然科学版,2013,27(1):51-56

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