Skip to main content
SpringerLink
Log in
Book cover

JSAI International Symposium on Artificial Intelligence

JSAI-isAI 2013: New Frontiers in Artificial Intelligence pp 214–229Cite as

Answering Legal Questions by Mining Reference Information

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8417))

Abstract

This paper presents a study on exploiting reference information to build a question answering system restricted to the legal domain. Most previous research focuses on answering legal questions whose answers can be found in one document (The term ‘documents’ corresponds to articles, paragraphs, items, or sub-items according to the naming rules used in the legal domain.) without using reference information. However, there are many legal questions whose answers could not be found without linking information from multiple documents. This connection is represented by explicit or implicit references. To the best of our knowledge, this type of questions is not adequately considered in previous work. To cope with them, we propose a novel approach which allow us to exploit the reference information among legal documents to find answers. This approach also uses requisite-effectuation structures of legal sentences and some effective similarity measures to support finding correct answers without training data. The experimental results showed that the proposed method is quite effective and outperform a traditional QA method, which does not use reference information.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://trect.nist.gov

  2. 2.

    http://www.clef-campaign.org

  3. 3.

    The reason why they call them high-level is that each requisite part or effectuation part consists of several logical parts.

  4. 4.

    To understand more about four cases of legal sentences and their logical parts, please check the paper of Bach et al. [2].

  5. 5.

    We can use these characteristics for a QA system as shown in Tomura, K., A study on a question answering system for laws, Master thesis, JAIST, 2013. The system answers to a question based on only one document.

  6. 6.

    http://code.google.com/p/cabocha/

  7. 7.

    http://www.ranks.nl/stopwords/japanese.html

  8. 8.

    We used synonym list extracted from Japanese WordNet Copyright 2009, 2012 by National Institute of Information and Communications Technology (NiCT).

  9. 9.

    The paragraph which contains the reference referring to the referenced paragraph.

  10. 10.

    This tool got the accuracy of \(\sim \) 90 %.

  11. 11.

    We did not count the number of definition in parentheses and only count paragraph main sentences.

References

  1. Anne, R.D., Yilmazel, O., Liddy, E.D.: Evaluation of restricted domain question-answering systems. In: Proceedings of Workshop on Question Answering in Restricted Domains, ACL, pp. 2–7 (2004)

    Google Scholar 

  2. Bach, N.X., Minh, N.L., Oanh, T.T., Shimazu, A.: A two-phase framework for learning logical structures of paragraphs in legal articles. J. ACM TALIP 12((1, article no 3)), 1–32 (2013)

    Google Scholar 

  3. Bach, N.X., Minh, N.L., Shimazu, A.: Recognition of requisite part and effectuation part in law sentences. In: Proceedings of ICCPOL, pp. 29–34 (2010)

    Google Scholar 

  4. Bach, N.X., Minh, N.L., Shimazu, A.: Rre task: The task of recognition of requisite part and effectuation part in law sentences. J. IJCPOL 23(2), 109–130 (2010)

    Google Scholar 

  5. Doan-Nguyen, H., Kosseim, L.: The problem of precision in restricted-domain question-answering. In: Proceedings of Workshop on Question Answering in Restricted Domains, ACL, pp. 8–15 (2004)

    Google Scholar 

  6. Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Prentice Hall Series in Artificial Intelligence, 2nd edn. Prentice Hall, Upper Saddle River (2009)

    Google Scholar 

  7. Ciaramita, M., Surdeanu, M., Zaragoza, H.: Learning to rank answers on large online qa collections. In: Proceedings of the ACL-HLT, pp. 719–727 (2008)

    Google Scholar 

  8. Manning, C.D., Raghana, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  9. Monroy, A., Calvo, H., Gelbukh, A.: NLP for shallow question answering of legal documents using graphs. In: Gelbukh, A. (ed.) CICLing 2009. LNCS, vol. 5449, pp. 498–508. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. Paik, J.H.: A novel tf-idf weighting scheme for effective ranking. In: Proceedings of ACM SIGIR, pp. 343–352 (2013)

    Google Scholar 

  11. Paltoglo, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of ACL, pp. 1386–1395 (2010)

    Google Scholar 

  12. Paulo, Q., Rodrigues, I.P.: A question-answering system for portuguese juridical documents. In: Proceedings of the ICAIL, pp. 256–257 (2005)

    Google Scholar 

  13. Peñas, A.: Overview of ResPubliQA 2009: question answering evaluation over european legislation. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Peñas, A., Roda, G. (eds.) CLEF 2009 Workshop, Part I. LNCS, vol. 6241, pp. 174–196. Springer, Heidelberg (2010)

    Google Scholar 

  14. Robertson, S., Walker, S.: On relevance weights with little relevance information. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 16–24 (1997)

    Google Scholar 

  15. Robertson, S., Zaragoza, S.: The probabilistic relevance framework: Bm25 and beyond. J. Found. Trends Inf. Retrieval 3, 333–389 (2009)

    Article  Google Scholar 

  16. Takano, K., Nakamura, M., Oyama, Y., Shimazu, A.: Semantic analysis of paragraphs consisting of multiple sentences - towards development of a logical formulation system. In: Proceedings of JURIX, pp. 117–126 (2010)

    Google Scholar 

  17. Tanaka, K., Kawazoe, I., Narita, H.: Standard structure of legal provisions for the legal knowledge processing by natural language. In Research report on Natural Language Processing, IPSJ, pp. 79–86 (1993) (in Japanese)

    Google Scholar 

  18. Tomura, K.: Study on question answering system for laws. Technical report, School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) (2013)

    Google Scholar 

  19. Tran, O.T., Ngo, B.X., Le Nguyen, M., Shimazu, A.: Reference resolution in legal texts. In: Proceedings of ICAIL, pp. 101–110 (2013)

    Google Scholar 

  20. Zhang, W., Yoshida, T., Tang, X.: A comparative study of TF*IDF, LSI and multi-words for text classification. Expert Syst. Appl. Int. J. 38, 2758–2765 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan

    Oanh Thi Tran, Bach Xuan Ngo, Minh Le Nguyen & Akira Shimazu

Authors
  1. Oanh Thi Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bach Xuan Ngo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minh Le Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akira Shimazu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oanh Thi Tran .

Editor information

Editors and Affiliations

  1. Seikei University, Musashino-shi, Tokyo, Japan

    Yukiko Nakano

  2. National Institute of Informatics Research Division, Tokyo, Japan

    Ken Satoh

  3. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Tran, O.T., Ngo, B.X., Le Nguyen, M., Shimazu, A. (2014). Answering Legal Questions by Mining Reference Information. In: Nakano, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2013. Lecture Notes in Computer Science(), vol 8417. Springer, Cham. https://doi.org/10.1007/978-3-319-10061-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10061-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10060-9

  • Online ISBN: 978-3-319-10061-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation