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A relation extraction method of Chinese named entities based on location and semantic features

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Abstract

Named entity relations are a foundation of semantic networks, ontology and the semantic Web, and are widely used in information retrieval and machine translation, as well as automatic question and answering systems. In named entity relations, relational feature selection and extraction are two key issues. The location features possess excellent computability and operability, while the semantic features have strong intelligibility and reality. Currently, relation extraction of Chinese named entities mainly adopts the Vector Space Model (VSM), a traditional semantic computing or the classification method, and these three methods use either the location features or the semantic features alone, resulting in unsatisfactory extraction. A relation extraction method of Chinese named entities called LaSE is proposed to combine the information gain of the positions of words and semantic computing based on HowNet. LaSE is scalable, semi-supervised and domain independent. Extensive experiments show that LaSE is superior, with an F-score of 0.879, which is at least 0.113 better than existing extraction methods that use either the location features or the semantic features alone.

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References

  1. ACE. http://www.nist.gov/speech/tests/ace/

  2. Chinchor NA (1998) Overview of MUC-7/MET-2. In: Proc of the 7th message understanding conference

    Google Scholar 

  3. Haspelmath M (2001) Word classes and parts of speech. In: Proc of the international encyclopedia of the social and behavioral sciences, Amsterdam, Holland, pp 16538–16545

    Chapter  Google Scholar 

  4. Zhang HP, Yu HK, Xiong DY, Liu Q (2003) HHMM-based Chinese lexical analyzer ICTCLAS. In: Proc of the 2nd SIGHAN workshop at ACL-41, Sapporo, Japan, pp 184–187

    Chapter  Google Scholar 

  5. Wang L, Che W, Liu T (2009) An SVMTool-based Chinese POS tagger. J Chin Inf Process 23:16–21

    Google Scholar 

  6. He TT, Xu C, Li J (2006) Named entity relation extraction method based on seed self-expansion. Comput Eng 32:183–184

    Google Scholar 

  7. Deng B, Fan XZ, Yang LG (2007) Entity relation extraction method using semantic pattern. Comput Eng 33:212–214

    Google Scholar 

  8. Liu L, Li BC, Zhang XF (2008) Named entity relation extraction based on SVM training by positive and negative cases. J Comput Appl 6:1444–1446

    Google Scholar 

  9. Zhou G, Su J, Zhang J, Zhang M (2005) Exploring various knowledge in relation extraction. In: Proc of 43th annual meeting of the association for computational linguistics, USA

    Google Scholar 

  10. Chen JX, Ji DH, Tan CL, Niu XY (2006) Relation extraction using label propagation based semi-supervised learning. In: Proc of the 44th annual meeting of the ACL, pp 129–136

    Google Scholar 

  11. Zhao Y, Qin B, Liu T (2010) Appraisal expression recognition with syntactic path for sentence sentiment classification. Int J Comput Process Orient Lang

  12. Che W, Liu T, Li Y (2010) Improving semantic role labeling with word sense. In: Proc of the 2010 annual conference of the North American chapter of the association for computational linguistics (NAACL-2010), Los Angeles, USA, pp 246–249

    Google Scholar 

  13. Che W, Liu T (2010) Using word sense disambiguation for semantic role labeling. In: Proc of the 4th international universal communication symposium (IUCS-2010), pp 166–173

    Google Scholar 

  14. Qin B, Zhao Y, Ding X (2010) Event type recognition based on trigger expansion. Tsinghai Sci Technol 15:251–258

    Article  Google Scholar 

  15. Xu J, Zhang ZX, Wu ZX (2008) Review on techniques of entity relation extraction. New technology of library and information service, pp 18–23

  16. Brin S (1998) Extracting patterns and relations from the WWW. In: Proc of the WebDB workshop at 6th international conference on extending database technology, Valencia, Spain, pp 172–183

    Google Scholar 

  17. Agichtein E, Gravano L (2000) Snowball: extracting relations from large plain-text collections. In: Proc of the 5th ACM international conference on digital libraries, Texas, USA

    Google Scholar 

  18. Etzioni O, Cafarella M, Downey D (2005) Unsupervised named-entity extraction from the Web: an experimental study. Artif Intell 165:91–134

    Article  Google Scholar 

  19. Aone C, Santacruz MR (2000) REES: a large-scale relation and event extraction system. In: Proc of the 6th applied natural language processing conference, New York, USA, pp 76–83

    Chapter  Google Scholar 

  20. Banko M, Cafarella MJ, Soderland S (2007) Open information extraction from the Web. In: Proc of the IJCAI-2007, Hyderabad, India, pp 2670–2676

    Google Scholar 

  21. Downey D, Etzioni O, Soderland S (2005) A probabilistic model of redundancy in information extraction. In: Proc of the IJCAI-2005, Scotland, UK

    Google Scholar 

  22. Iria J (2005) T-Rex: a flexible relation extraction framework. In: Proc of the 8th annual colloquium for the UK special interest group for computational linguistics (CLUK-2005), Manchester, UK

    Google Scholar 

  23. Iria J, Ciravegna F (2005) Relation extraction for mining the semantic web. In: Proc of the machine learning for the semantic web Dagstuhl seminar. Dagstuhl, Germany

    Google Scholar 

  24. Specia L, Motta E (2006) A hybrid approach for extracting semantic relations from texts. In: Proc of the 2nd workshop on ontology learning and population at COLING/ACL-2006, Sydney, Australia, pp 57–64

    Google Scholar 

  25. Banko M, Etzioni O (2008) The tradeoffs between open and traditional relation extraction. In: Proc of the ACL-2008, pp 28–36

    Google Scholar 

  26. Ngai G, Florian R (2001) Transformation-based learning in the first lane. In: Proc of the NAACL-2001, pp 40–47

    Google Scholar 

  27. Wang JS, Byrnes J, Valtorta M, Huhns M (2012) On the combination of logical and probabilistic models for information analysis. Int J Appl Intell 36:472–497

    Article  Google Scholar 

  28. Daybelge T, Cicekli I (2011) A ranking method for example based machine translation results by learning from user feedback. Int J Appl Intell 35:296–321

    Article  Google Scholar 

  29. Wong TL (2011) Learning to adapt cross language information extraction wrapper. Int J Appl Intell

  30. Dong J, Sun L, Feng YY (2007) Chinese automatic entity relation extraction. J Chin Inf Process 21:80–85

    Google Scholar 

  31. Li H, Pan Z, Duan L, Chen J (2010) A new feature weight strategy in Chinese question classification. J Comput Inf Syst 6:113–119

    Google Scholar 

  32. Liu Z, Zhu C, Zhao T (2010) Chinese named entity recognition with a sequence labeling approach: based on characters or based on words. In: Advanced intelligent computing theories and applications. With aspects of artificial intelligence, vol 6216, pp 634–640

    Google Scholar 

  33. Gao XX, Kuang T (2011) Research of word similarity model based on HowNet. In: Proc of the EMEIT-2011, pp 287–290

    Google Scholar 

  34. Liu QL, Gu XF, Li JP (2010) Researches of Chinese sentence similarity based on HowNet. In: Proc of the ICACIA-2010, pp 26–29

    Google Scholar 

  35. Jing LP, Ng MK, Huang JZ (2010) Knowledge-based vector space model for text clustering. Knowl Inf Syst 25:35–55

    Article  Google Scholar 

  36. Tamine LL, Boughanem M, Daoud M (2010) Evaluation of contextual information retrieval effectiveness: overview of issues and research. Knowl Inf Syst 24:1–34

    Article  Google Scholar 

  37. Li SJ, Zhang J, Huang X (2002) Semantic computation in a Chinese question answering system. J Comput Sci Technol 17:933–939

    Article  MATH  Google Scholar 

  38. Lu S, Bai S, Huang X (2002) An unsupervised approach to word sense disambiguation based on sense-words in vector space model. J Softw 13:1082–1089

    Google Scholar 

  39. Dorji TC, Atlam ES, Yata S, Fuketa M, Morita K, Aoe JI (2011) Extraction, selection and ranking of field association (FA) terms from domain-specific corpora for building a comprehensive FA terms dictionary. Knowl Inf Syst 27:141–161

    Article  Google Scholar 

  40. Yang SH, Hu BG (2011) Discriminative feature selection by nonparametric Bayes error minimization. Knowl Inf Syst

  41. Zhang X, Chen GQ, Wei Q (2011) Building a highly-compact and accurate associative classifier. Int J Appl Intell 34:74–86

    Article  Google Scholar 

  42. Zhang R, Tran T (2011) An information gain-based approach for recommending useful product reviews. Knowl Inf Syst 26:419–434

    Article  Google Scholar 

  43. Wang B (1999) Auto-alignment study of Chinese-English bilingual corpora. PhD thesis, Institute of Computing Technology, Chinese Academy of Sciences

  44. Pham MQN, Nguyen ML, Ngo BX, Shimazu A (2012) A learning-to-rank method for information updating task. Int J Appl Intell

  45. Li JH, Zhou GD (2011) Unified semantic role labeling for verbal and nominal predicates in Chinese language. ACM Trans Asian Lang Inf Process 10:13:1–13:21

    MATH  Google Scholar 

  46. Liu C, Li SJ (2002) Word similarity computing based on HowNet. Comput Linguist Chin Lang Process 7:59–76

    Google Scholar 

  47. Li L, Ogihara M, Ma S (2010) On combining multiple clusterings: an overview and a new perspective. Int J Appl Intell 33:207–219

    Article  Google Scholar 

  48. Liu YH, Yang CS, Yang YB, Lin FH, Du XM, Ito T (2012) Case learning for CBR-based collision avoidance systems. Int J Appl Intell 36:308–319

    Article  Google Scholar 

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Authors and Affiliations

  1. The University of Vermont, Burlington, VT, 05405, USA

    Haiguang Li, Xindong Wu & Zhao Li

  2. Hefei University of Technology, Hefei, Anhui, 230009, China

    Gongqing Wu

Authors
  1. Haiguang Li
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  2. Xindong Wu
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  3. Zhao Li
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  4. Gongqing Wu
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Corresponding author

Correspondence to Haiguang Li.

Additional information

This work is supported by the National High Technology Research and Development Program of China (863 Program) under grant 2012AA011005; the National Natural Science Foundation of China (NSFC) under grants 60828005 and 60975034; and the Natural Science Foundation of Anhui Province of China under grant 090412044. An earlier version of this paper was presented at the 2009 IEEE International Conference on Granular Computing.

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Li, H., Wu, X., Li, Z. et al. A relation extraction method of Chinese named entities based on location and semantic features. Appl Intell 38, 1–15 (2013). https://doi.org/10.1007/s10489-012-0353-0

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