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Towards Annotation of Legal Documents with Ontology Concepts

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AI Approaches to the Complexity of Legal Systems (AICOL 2015, AICOL 2016, AICOL 2016, AICOL 2017, AICOL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10791))

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Abstract

This paper describes a task of semantic labeling of document segments. The idea exploits ontology in providing a fine-grained conceptual document annotation. We describe a way of dividing a document into its constituent semantically-coherent blocks. These blocks are then used to perform conceptual tagging for efficient passage information retrieval. The proposed task interfaces other application areas such as intra-mapping of ontologies, text summarization and information extraction. The system has been evaluated on a task of conceptual tagging of documents and achieved a promising result.

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Notes

  1. 1.

    https://eur-lex.europa.eu/homepage.html.

  2. 2.

    E.g homonyms and synonyms.

  3. 3.

    http://eurovoc.europa.eu/.

  4. 4.

    An online database of EU government documents.

  5. 5.

    We used WordNet concept distance to compute semantic similarity between adjacent sentences of a candidate segment according to a chosen window.

  6. 6.

    E.g., synonymy and polysemy.

  7. 7.

    Where k is a chosen number and set as 3.

  8. 8.

    English wikipedia dump was downloaded on July 30, 2015.

  9. 9.

    https://code.google.com/p/word2vec/.

  10. 10.

    https://github.com/RaRe-Technologies/gensim. Training Parameters: Context Window: 5, Neural Network layer size: 200, Minimum word count: 5.

  11. 11.

    Ensures the highest similarity is at least z percent higher than the next highest value. By default, z = 0.3.

References

  1. Adebayo, K., Di Caro, L., Boella, G.: NORMAS at SemEval-2016 task 1: SEMSIM: a multi-feature approach to semantic text similarity. In: Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2016, San Diego, CA, USA, 16–17 June 2016, pp. 718–725 (2016)

    Google Scholar 

  2. Adebayo, K.J., Di Caro, L., Boella, G.: Text segmentation with topic modeling and entity coherence. In: Abraham, A., Haqiq, A., Alimi, A.M., Mezzour, G., Rokbani, N., Muda, A.K. (eds.) HIS 2016. AISC, vol. 552, pp. 175–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52941-7_18

    Chapter  Google Scholar 

  3. Adebayo, K.J., Di Caro, L., Boella, G., Bartolini, C.: An approach to information retrieval and question answering in the legal domain, pp. 15–25 (2016)

    Google Scholar 

  4. Ai, Q., Yang, L., Guo, J., Croft, W.B.: Improving language estimation with the paragraph vector model for ad-hoc retrieval. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 869–872. ACM (2016)

    Google Scholar 

  5. Bikakis, N., Giannopoulos, G., Dalamagas, T., Sellis, T.: Integrating keywords and semantics on document annotation and search. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6427, pp. 921–938. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16949-6_19

    Chapter  Google Scholar 

  6. Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 113–120. ACM (2006)

    Google Scholar 

  7. Choi, F.Y.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference, pp. 26–33. Association for Computational Linguistics (2000)

    Google Scholar 

  8. Clinchant, S., Perronnin, F.: Aggregating continuous word embeddings for information retrieval. In: Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, pp. 100–109 (2013)

    Google Scholar 

  9. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: A framework and graphical development environment for robust NLP tools and applications. In: ACL, pp. 168–175 (2002)

    Google Scholar 

  10. Dill, S., et al.: A case for automated large-scale semantic annotation. Web Semant.: Sci. Serv. Agents World Wide Web 1(1), 115–132 (2003)

    Article  Google Scholar 

  11. Halliday, M.A.K., Hasan, R.: Cohesion in English (1976)

    Google Scholar 

  12. Handschuh, S., Staab, S.: Authoring and annotation of web pages in cream. In: Proceedings of the 11th International Conference on World Wide Web, pp. 462–473. ACM (2002)

    Google Scholar 

  13. Hearst, M.A.: TextTiling: a quantitative approach to discourse segmentation. Technical report, Citeseer (1993)

    Google Scholar 

  14. Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Computational linguistics 23(1), 33–64 (1997)

    Google Scholar 

  15. Kiyavitskaya, N., Zeni, N., Mich, L., Cordy, J.R., Mylopoulos, J.: Text mining through semi automatic semantic annotation. In: Reimer, U., Karagiannis, D. (eds.) PAKM 2006. LNCS (LNAI), vol. 4333, pp. 143–154. Springer, Heidelberg (2006). https://doi.org/10.1007/11944935_13

    Chapter  Google Scholar 

  16. Laclavík, M., Ciglan, M., Seleng, M., Krajei, S.: Ontea: semi-automatic pattern based text annotation empowered with information retrieval methods. In: Tools for Acquisition, Organisation and Presenting of Information and Knowledge: Proceedings in Informatics and Information Technologies, Kosice, Vydavatelstvo STU, Bratislava, part, vol. 2, pp. 119–129 (2007)

    Google Scholar 

  17. Laclavik, M., Seleng, M., Gatial, E., Balogh, Z., Hluchy, L.: Ontology based text annotation-OnTeA, pp. 280–284 (2006)

    Google Scholar 

  18. Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053 (2014)

  19. Li, Y., McLean, D., Bandar, Z.A., O’shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18(8), 1138–1150 (2006)

    Article  Google Scholar 

  20. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)

    Google Scholar 

  21. Loza Mencía, E., Fürnkranz, J.: Efficient multilabel classification algorithms for large-scale problems in the legal domain. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS (LNAI), vol. 6036, pp. 192–215. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12837-0_11

    Chapter  Google Scholar 

  22. Mitra, B., Diaz, F., Craswell, N.: Learning to match using local and distributed representations of text for web search. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1291–1299. International World Wide Web Conferences Steering Committee (2017)

    Google Scholar 

  23. Popov, B., Kiryakov, A., Kirilov, A., Manov, D., Ognyanoff, D., Goranov, M.: KIM – semantic annotation platform. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 834–849. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39718-2_53

    Chapter  Google Scholar 

  24. Presutti, V., Draicchio, F., Gangemi, A.: Knowledge extraction based on discourse representation theory and linguistic frames. In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 114–129. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33876-2_12

    Chapter  Google Scholar 

  25. Riedl, M., Biemann, C.: Text segmentation with topic models. J. Lang. Technol. Comput. Linguist. 27(1), 47–69 (2012)

    Google Scholar 

  26. Turney, P.D., Pantel, P., et al.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37(1), 141–188 (2010)

    Article  MathSciNet  Google Scholar 

  27. Usbeck, R., et al.: GERBIL: general entity annotator benchmarking framework. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1133–1143. ACM (2015)

    Google Scholar 

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Acknowledgements

Kolawole J. Adebayo has received funding from the Erasmus Mundus Joint International Doctoral (Ph.D.) programme in Law, Science and Technology. Luigi Di Caro and Guido Boella have received funding from the European Union’s H2020 research and innovation programme under the grant agreement No 690974 for the project “MIREL: MIning and REasoning with Legal texts”.

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Authors and Affiliations

  1. Dipartimento di Informatica, Universita Di Torino, Corso Svizzera 185, 10149, Turin, Italy

    Kolawole John Adebayo, Luigi Di Caro & Guido Boella

Authors
  1. Kolawole John Adebayo
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  2. Luigi Di Caro
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  3. Guido Boella
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Corresponding author

Correspondence to Kolawole John Adebayo .

Editor information

Editors and Affiliations

  1. University of Turin, Turin, Italy

    Ugo Pagallo

  2. University of Bologna, Bologna, Italy

    Monica Palmirani

  3. La Trobe University, Melbourne, VIC, Australia

    Pompeu Casanovas

  4. University of Bologna, Bologna, Italy

    Giovanni Sartor

  5. Inria - Sophia Antipolis-Méditerranée, Sophia Antipolis, France

    Serena Villata

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Adebayo, K.J., Di Caro, L., Boella, G. (2018). Towards Annotation of Legal Documents with Ontology Concepts. In: Pagallo, U., Palmirani, M., Casanovas, P., Sartor, G., Villata, S. (eds) AI Approaches to the Complexity of Legal Systems. AICOL AICOL AICOL AICOL AICOL 2015 2016 2016 2017 2017. Lecture Notes in Computer Science(), vol 10791. Springer, Cham. https://doi.org/10.1007/978-3-030-00178-0_23

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  • DOI: https://doi.org/10.1007/978-3-030-00178-0_23

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