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SREC: Discourse-level semantic relation extraction from text

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Abstract

Semantic relation extraction is a significant topic in semantic web and natural language processing with various important applications such as knowledge acquisition, web and text mining, information retrieval and search engine, text classification and summarization. Many approaches such rule base, machine learning and statistical methods have been applied, targeting different types of relation ranging from hyponymy, hypernymy, meronymy, holonymy to domain-specific relation. In this paper, we present a computational method for extraction of explicit and implicit semantic relation from text, by applying statistic and linear algebraic approaches besides syntactic and semantic processing of text.

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Notes

  1. Available at: http://tartarus.org/~martin/PorterStemmer/.

  2. Available at: http://cogcomp.cs.illinois.edu/page/software_view/3.

  3. Available at: http://cogcomp.cs.illinois.edu/page/software_view/12.

  4. http://www.ldc.upenn.edu/.

References

  1. Beisswanger E (2010) Exploiting relation extraction for ontology alignment. In: Presented at the 9th international semantic web conference (ISWC2010), Shanghai

  2. Iria J, Ciravegna F (2005) Relation extraction for mining the semantic web. In: Presented at the machine learning for the semantic web, Dagstuhl

  3. Karoui L et al (2007) Analyses and fundamental ideas for a relation extraction approach. In: IEEE 23rd international conference on data engineering (ICDEW ‘07)

  4. Imsombut A (2009) A statistical approach for semantic relation extraction. In: Eighth international symposium on natural language processing, Bangkok

  5. Khoo CSG et al (2001) Using cause–effect relations in text to improve information retrieval precision. Inf Process Manag Int J arch 37(1):119–145

    Article  MathSciNet  MATH  Google Scholar 

  6. Had M et al (2009) Relation extraction for monitoring economic networks. In: 14th international conference on applications of natural language to information systems (NLDB 2009), Saarbrücken

  7. Manine A-P et al (2009) Learning ontological rules to extract multiple relations of genic interactions from text. Int J Med Inf 78:e31–e38

    Article  Google Scholar 

  8. Khoo CSG, Na J-C (2006) Annual review of information science and technology, vol 40. Wiley, New York, pp 157–228

    Google Scholar 

  9. Wittgenstein L (1953) Philosophical investigations. MacMillan, New York

    Google Scholar 

  10. Reynaud C, Safar B (2007) Exploiting wordnet as background knowledge. In: The 6th international semantic web conference and the 2nd Asian semantic web conference, Busan

  11. Halliday MAK, Hasan R (1976) Cohesion in English. Longman, London

    Google Scholar 

  12. Yangarber R (2003) Counter-training in the discovery of semantic patterns. In: 41st annual meeting of the association for computational linguistics (ACL-03), Sapporo, pp 343–350

  13. Stevenson M, Greenwood MA (2005) Asemantic approach to IE pattern induction. In: 43rd annual meeting of the association for computational linguistics, Ann Arbor, pp 379–386

  14. Sudo K et al (2003) An improved extraction pattern representation model for automatic IE pattern acquisition. In: 41st annual meeting of the association for computational linguistics (ACL-03), Sapporo, pp 224–231

  15. Miller S et al (2000) A novel use of statistical parsing to extract information from text. In: Sixth applied natural language processing conference, pp 226–233

  16. Gamallo P et al (2002) Mapping syntactic dependencies onto semantic relations. In: Presented at the ECAI workshop on machine learning and natural language processing for ontology engineering, Lyon

  17. Nedellec C (2000) Corpus-based learning of semantic relations by the ILP system, ASIUM. Learn Lang Log Lect Notes Comput Sci 1925:259–278

    Article  Google Scholar 

  18. Zelenko D et al (2003) Kernel methods for relation extraction. Mach Learn Res 3:1083–1106

    MathSciNet  MATH  Google Scholar 

  19. Zhao S, Grishman R (2005) Extracting relations with integrated information using kernel methods. In: 43rd annual meeting on association for computational linguistics. Ann Arbor

  20. Reichartz F et al (2009) Dependency tree kernels for relation extraction from natural language text. In: Presented at the European conference on machine learning and knowledge discovery in databases(ECML PKDD ‘09), Bled

  21. Zhang M et al (2006) A composite kernel to extract relations between entities with both flat and structured features. In: International conference on computational linguistics and the annual meeting of the association for computational linguistics (COLING-ACL’2006), Sydney, pp 825–832

  22. Zhang M et al (2008) Exploring syntactic structured features over parse trees for relation extraction using kernel methods. Inf Process Manag 44:687–701

    Article  Google Scholar 

  23. Zhou GD, Zhang M (2007) Extracting relation information from text documents by exploring various types of knowledge. Inf Process Manag 43:969–982

    Article  Google Scholar 

  24. Maedche A, Staab S (2001) Ontology learning for the semantic web. IEEE Intell Syst 16(2):72–79

    Article  Google Scholar 

  25. Reinberger ML et al (2004) Automatic initiation of an ontology. In: Move to meaningful internet systems, Napa, pp 600–617

  26. Yamaguchi T (2001) Acquiring conceptual relations from domain-specific texts. In: IJCAI 2001 workshop on ontology learning (OL’2001), Seattle

  27. Kavalec M, Svatek V (2005) A study on automated relation labelling in ontology learning. In: Buitelaar P, Cimiano P, Magnini B (eds) A study of automated relation labelling in ontology learning, ontology learning from text: methods, evaluation and applications. IOS Press, Amsterdam, pp 44–58

    Google Scholar 

  28. Schutz A, Buitelaar P (2005) RelExt: a tool for relation extraction from text in ontology extension. In: 4th international semantic web conference, Galway

  29. Yan Y et al (2009) An integrated approach for relation extraction from Wikipedia texts. In: Presented at the content analysis in web 2.0(CAW2), Madrid

  30. Landauer TK, Dumais ST (1997) A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychol Rev 104(2):211–240

    Article  Google Scholar 

  31. Kontostathis A et al (2005) Identification of critical values in latent semantic indexing. Found Data Min Knowl Discov 6:333–346

    Google Scholar 

  32. Marquez L et al (2008) Semantic role labeling: an introduction to the special issue. Comput Linguist 34:1–16

    Article  Google Scholar 

  33. Hofstadter DR (1999) Gödel, escher, bach: an eternal golden braid. Art of Mentoring Series, Basic Books, New York

  34. Porter MF (1980) An algorithm for suffix stripping. Program 14(3):130–137

    Article  Google Scholar 

  35. Greengrass E (2000) Information retrieval: a survey. Technical report, Department of defence, Washington

  36. Quirk C et al (2004) Monolingual machine translation for paraphrase generation. In: Conference on empirical methods in natural language processing, Barcelona

  37. Zhou GD et al (2010) Tree kernel-based semantic relation extraction with rich syntactic and semantic information. Inf Sci 180:1313–1325

    Article  Google Scholar 

  38. Bunescu R, Mooney RJ (2005) A shortest path dependency kernel for relation extraction. In: Human language technology conference and conference on empirical methods in natural language (HLT/EMNLP’2005), Vancouver, pp 724–731

  39. Culotta A, Sorensen J (2004) Dependency tree kernels for relation extraction. Annual meeting of the association for computational linguistics (ACL’04), Barcelona, pp 423–429

  40. Zhou GD et al (2005) Exploring various knowledge in relation extraction. Annual meeting of the association for computational linguistics (ACL’2005), Michigan, pp 427–434

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Acknowledgments

Authors thank referees for their careful reading of the manuscript and suggestions.

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Authors and Affiliations

  1. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Mohammad-hadi Zahedi & Mohsen Kahani

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  1. Mohammad-hadi Zahedi
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  2. Mohsen Kahani
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Correspondence to Mohammad-hadi Zahedi.

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Zahedi, Mh., Kahani, M. SREC: Discourse-level semantic relation extraction from text. Neural Comput & Applic 23, 1573–1582 (2013). https://doi.org/10.1007/s00521-012-1109-9

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