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An intensive case study on kernel-based relation extraction

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Abstract

Relation extraction refers to a method of efficiently detecting and identifying predefined semantic relationships within a set of entities in text documents. Numerous relation extractionfc techniques have been developed thus far, owing to their innate importance in the domain of information extraction and text mining. The majority of the relation extraction methods proposed to date is based on a supervised learning method requiring the use of learning collections; such learning methods can be classified into feature-based, semi-supervised, and kernel-based techniques. Among these methods, a case analysis on a kernel-based relation extraction method, considered the most successful of the three approaches, is carried out in this paper. Although some previous survey papers on this topic have been published, they failed to select the most essential of the currently available kernel-based relation extraction approaches or provide an in-depth comparative analysis of them. Unlike existing case studies, the study described in this paper is based on a close analysis of the operation principles and individual characteristics of five vital representative kernel-based relation extraction methods. In addition, we present deep comparative analysis results of these methods. In addition, for further research on kernel-based relation extraction with an even higher performance and for general high-level kernel studies for linguistic processing and text mining, some additional approaches including feature-based methods based on various criteria are introduced.

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Notes

  1. Defense Advanced Research Projects Agency of the U.S.

  2. National Institute of Standards and Technology of the U.S.

  3. Dependency grammar relation, parse tree, etc. between words in a sentence.

  4. Binary classification for identifying the possible relation between two named entities.

  5. Relation extraction for all instances with relations shown in the relation identification results.

References

  1. ACE, “Automatic Content Extraction,” 2009. [Online]. Available: http://www.itl.nist.gov/iad/mig//tests/ace/.

  2. Agichtein E, and Gravano L (2000) “Snowball: extracting relations from large plain-text collections,” in Proceedings of the fifth ACM conference on Digital libraries, pp. 85–94.

  3. Aone C, and Ramos-Santacruz M (2000) “REES: a large-scale relation and event extraction system,” in The 6th Applied Natural Language Processing Conference

  4. Bach N, and Badaskar S (2007) “A survey on relation extraction,” Literature review for Language and Statistics II

  5. Brin S (1999) Extracting patterns and relations from the World Wide Web. Lect Notes Comput Sci 1590/1999:172–183

    Article  Google Scholar 

  6. Bunescu R, and Mooney RJ (2005) “A shortest path dependency kernel for relation extraction,” in Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 724–731

  7. Bunescu R, and Mooney RJ (2006) “Subsequence kernels for relation extraction,” in Proceeding of the Ninth Conference on Natural Language Learning (CoNLL-2005)

  8. Charniak E (2001) “Immediate-head parsing for language models,” in Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

  9. Choi S-P, Jeong C-H, Choi Y-S, Myaeng S-H (2009) Relation extraction based on extended composite kernel using flat lexical features. J KIISE: Softw Appl 36:8

    Google Scholar 

  10. Collins M (1997) “Three generative, lexicalised models for statistical parsing,” in Proceedings of the 35th Annual Meeting of the ACL (jointly with the 8th Conference of the EACL), Madrid

  11. Collins M, and Duffy N (2001) “Convolution kernels for natural language,” in NIPS-2001

  12. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  13. Cristianini N, and Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press

  14. Culotta A, and Sorensen J (2004) “Dependency tree kernels for relation extraction,” in Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics

  15. Etzioni O, Cafarella M, Downey D, Popescu A-M, Shaked T, Soderland S, Weld DS, Yates A (2005) Unsupervised named-entity extraction from the Web: an experimental study. Artif Intell 165(1):91–134

    Article  Google Scholar 

  16. Fellbaum C (1998) WordNet: an electronic lexical database. MIT Press, Cambridge

    MATH  Google Scholar 

  17. Freund Y, Schapire RE (1999) Large margin classification using the perceptron algorithm. Mach Learn 37(3):277–296

    Article  MATH  Google Scholar 

  18. Fundel K, Küffner R, Zimmer R (2007) RelEx—Relation extraction using dependency parse trees. Bioinformatics 23(3):365–371

    Article  Google Scholar 

  19. Gartner T, Flach P, and Wrobel S (2003) “On graph kernels: hardness results and efficient alternatives,” Learning Theory and Kernel Machines, pp. 129–143

  20. Hockenmaier J, and Steedman M (2002) “Generative models for statistical parsing with combinatory categorial grammar,” in Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA

  21. Jiang J, and Zhai C (2007) “A systematic exploration of the feature space for relation extraction,” in NAACL HLT

  22. Joachims T (1998) “Text categorization with support vecor machine: learning with many relevant features,” in ECML-1998

  23. Kambhatla N (2004) “Combining lexical, syntactic and semantic features with Maximum Entropy models for extracting relations,” in ACL-2004

  24. Li J, Zhang Z, Li X, Chen H (2008) Kernel-based learning for biomedical relation extraction. J Am Soc Inf Sci Technol 59(5):756–769

    Article  MathSciNet  Google Scholar 

  25. Li W, Zhang P, Wei F, Hou Y, and Lu Q (2008) “A novel feature-based approach to Chinese entity relation extraction,” in Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, pp. 89–92

  26. Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C (2002) Text classification using string kernels. J Mach Learn Res 2:419–444

    MATH  Google Scholar 

  27. Mintz M, Bills S, Snow R, and Jurafsky D (2009) “Distant supervision for relation extraction without labeled data,” in 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 2:1003–1011

  28. Moncecchi G, Minel JL, and Wonsever D (2010) “A survey of kernel methods for relation extraction,” in Workshop on NLP and Web-based technologies (IBERAMIA 2010)

  29. Moschitti A (2004) “A study on convolution kernels for shallow semantic parsing,” in ACL-2004

  30. MUC, “The NIST MUC Website,” 2001. [Online]. Available: http://www.itl.nist.gov/iaui/894.02/related_projects/muc/.

  31. Nguyen DPT, Matsuo Y, and Ishizuka M (2007) “Exploiting syntactic and semantic information for relation extraction from wikipedia,” in IJCAI Workshop on Text-Mining & Link-Analysis (TextLink 2007)

  32. Nguyen T.-VT, Moschitti A, and Riccardi G (2009) “Convolution kernels on constituent, dependency and sequential structures for relation extraction,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing 3:1378–1387

  33. Ratnaparkhi A (1996) “A maximum entropy part-of-speech tagger,” in Proceedings of the Empirical Methods in Natural Language Processing Conference

  34. Reichartz F, Korte H, and Paass G (2009) “Dependency tree kernels for relation extraction from natural language text,” Machine Learning and Knowledge Discovery in Databases, pp. 270–285

  35. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386–408

    Article  MathSciNet  Google Scholar 

  36. TAC, “Text Analysis Conference,” 2012. [Online]. Available: http://www.nist.gov/tac/.

  37. Yarowsky D (1995) “Unsupervised word sense disambiguation rivaling supervised methods,” in Proceedings of the 33rd annual meeting on Association for Computational Linguistics pp. 189–196

  38. Yates A, Cafarella M, Banko M, Etzioni O, Broadhead M, and Soderland S (2007) “TextRunner: open information extraction on the web,” in Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 25–26

  39. Zelenco D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3:1083–1106

    MathSciNet  Google Scholar 

  40. Zhang M, Zhang J, and Su J (2006) “Exploring syntactic features for relation extraction using a convolution tree kernel,” in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pp. 288–295

  41. Zhang M, Zhang J, Su J, and Zhou G (2006) “A composite kernel to extract relations between entities with both flat and structured features,” in 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pp. 825–832

  42. Zhang M, Zhou G, Aiti A (2008) Exploring syntactic structured features over parse trees for relation extraction using kernel methods. Inf Process Manag 44(2):687–701

    Article  Google Scholar 

  43. Zhao S, and Grishman R (2005) “Extracting relations with integrated information using kernel methods,” in ACL-2005

  44. Zhou G, Su J, Zhang J, and Zhang M (2005) “Exploring various knowledge in relation extraction,” in ACL-2005

  45. Zhou G, Zhang M, Ji D, and Zhu Q (2007) “Tree Kernel-based relation extraction with context-sensitive structured parse tree information,” in The 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2007

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Authors and Affiliations

  1. Information S/W Research Center, Korea Institution of Science and Technology Information (KISTI), 335 Gwahangno, Yuseong-gu, Daejeon, South Korea

    Sung-Pil Choi, Seungwoo Lee, Hanmin Jung & Sa-kwang Song

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  1. Sung-Pil Choi
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  2. Seungwoo Lee
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  3. Hanmin Jung
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  4. Sa-kwang Song
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Correspondence to Sa-kwang Song.

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Choi, SP., Lee, S., Jung, H. et al. An intensive case study on kernel-based relation extraction. Multimed Tools Appl 71, 741–767 (2014). https://doi.org/10.1007/s11042-013-1380-5

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