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.
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References
Auger, A., Barriere, C.: Pattern-based approaches to semantic relation extraction: A state-of-the-art. Terminology 14(1), 1–19 (2008)
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)
Biemann, C.: Ontology learning from text: A survey of methods. In: LDV Forum, vol. 20, pp. 75–93 (2005)
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)
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)
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)
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)
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)
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)
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)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
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)
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)
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
Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)
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)
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)
Lesmo, L.: The Turin University Parser at Evalita 2009. Proceedings of EVALITA 9 (2009)
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)
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
Srinivasan, P., Rindflesch, T.: Exploring text mining from medline. In: Proceedings of the AMIA Symposium, p. 722. American Medical Informatics Association (2002)
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)
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Boella, G., Di Caro, L., Robaldo, L. (2013). Semantic Relation Extraction from Legislative Text Using Generalized Syntactic Dependencies and Support Vector Machines. In: Morgenstern, L., Stefaneas, P., Lévy, F., Wyner, A., Paschke, A. (eds) Theory, Practice, and Applications of Rules on the Web. RuleML 2013. Lecture Notes in Computer Science, vol 8035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39617-5_20
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DOI: https://doi.org/10.1007/978-3-642-39617-5_20
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