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Semantic Relation Extraction from Legislative Text Using Generalized Syntactic Dependencies and Support Vector Machines

  • Guido Boella
  • Luigi Di Caro
  • Livio Robaldo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)

Abstract

In this paper we present a technique to automatically extract semantic knowledge from legislative text. Instead of using pattern matching methods relying on lexico-syntactic patterns, we propose a technique which uses syntactic dependencies between terms extracted with a syntactic parser. The idea is that syntactic information are more robust than pattern matching approaches when facing length and complexity of the sentences. Relying on a manually annotated legislative corpus, we transform all the surrounding syntax of the semantic information into abstract textual representations, which are then used to create a classification model by means of a standard Support Vector Machine system. In this work, we initially focus on three different semantic tags, achieving very high accuracy levels on two of them, demonstrating both the limits and the validity of the approach.

Keywords

Automatic Semantic Annotation Semantic Information Extraction Dependency Parsing Support Vector Machines 

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References

  1. 1.
    Auger, A., Barriere, C.: Pattern-based approaches to semantic relation extraction: A state-of-the-art. Terminology 14(1), 1–19 (2008)CrossRefGoogle Scholar
  2. 2.
    Berland, M., Charniak, E.: Finding parts in very large corpora. In: Annual Meeting Association for Computational Linguistics, vol. 37, pp. 57–64. Association for Computational Linguistics (1999)Google Scholar
  3. 3.
    Biemann, C.: Ontology learning from text: A survey of methods. In: LDV Forum, vol. 20, pp. 75–93 (2005)Google Scholar
  4. 4.
    Boella, G., di Caro, L., Humphreys, L., Robaldo, L.: Using legal ontology to improve classification in the eunomos legal document and knowledge management system. In: Semantic Processing of Legal Texts Workshop (SPLeT 2012) at LREC 2012 (2012)Google Scholar
  5. 5.
    Boella, G., Humphreys, L., Martin, M., Rossi, P., van der Torre, L.: Eunomos, a legal document and knowledge management system to build legal services. In: Palmirani, M., Pagallo, U., Casanovas, P., Sartor, G. (eds.) AICOL Workshops 2011. LNCS (LNAI), vol. 7639, pp. 131–146. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Boella, G., Martin, M., Rossi, P., van der Torre, L., Violato, A.: Eunomos, a legal document and knowledge management system for regulatory compliance. In: Proceedings of Information Systems: A Crossroads for Organization, Management, Accounting and Engineering (ITAIS) Conference. Springer, Berlin (2012)Google Scholar
  7. 7.
    Boella, G., di Caro, L., Humphreys, L., Robaldo, L., van der Torre, L.: Nlp challenges for eunomos, a tool to build and manage legal knowledge. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC) (2012)Google Scholar
  8. 8.
    Boella, G., Di Caro, L., Humphreys, L.: Using classification to support legal knowledge engineers in the eunomos legal document management system. In: Fifth International Workshop on Juris-informatics (JURISIN) (2011)Google Scholar
  9. 9.
    Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: An overview. In: Ontology Learning from Text: Methods, Evaluation and Applications, vol. 123, pp. 3–12 (2005)Google Scholar
  10. 10.
    Candan, K., Di Caro, L., Sapino, M.: Creating tag hierarchies for effective navigation in social media. In: Proceedings of the 2008 ACM Workshop on Search in Social Media, pp. 75–82. ACM (2008)Google Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  12. 12.
    Fortuna, B., Mladenič, D., Grobelnik, M.: Semi-automatic construction of topic ontologies. In: Ackermann, M., et al. (eds.) EWMF/KDO 2005. LNCS (LNAI), vol. 4289, pp. 121–131. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Grabmair, M., Ashley, K.D., Hwa, R., Sweeney, P.M.: Toward extracting information from public health statutes using text classification machine learning. In: JURIX, pp. 73–82 (2011)Google Scholar
  14. 14.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009), http://doi.acm.org/10.1145/1656274.1656278 CrossRefGoogle Scholar
  15. 15.
    Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)Google Scholar
  16. 16.
    Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)Google Scholar
  17. 17.
    Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  18. 18.
    Lesmo, L.: The Turin University Parser at Evalita 2009. Proceedings of EVALITA 9 (2009)Google Scholar
  19. 19.
    Platt, J., et al.: Sequential minimal optimization: A fast algorithm for training support vector machines. In: Advances in Kernel Methods-Support Vector Learning, vol. 208, pp. 98–112 (1999)Google Scholar
  20. 20.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975), http://doi.acm.org/10.1145/361219.361220 zbMATHCrossRefGoogle Scholar
  21. 21.
    Srinivasan, P., Rindflesch, T.: Exploring text mining from medline. In: Proceedings of the AMIA Symposium, p. 722. American Medical Informatics Association (2002)Google Scholar
  22. 22.
    Yang, H., Callan, J.: Ontology generation for large email collections. In: Proceedings of the 2008 International Conference on Digital Government Research, pp. 254–261. Digital Government Society of North America (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guido Boella
    • 1
  • Luigi Di Caro
    • 1
  • Livio Robaldo
    • 1
  1. 1.Department of Computer ScienceUniversity of TurinTurinItaly

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