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Lexical-Morphological Modeling for Legal Text Analysis

  • Danilo S. CarvalhoEmail author
  • Minh-Tien Nguyen
  • Chien-Xuan Tran
  • Minh-Le Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10091)

Abstract

In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer. The proposed method is based on the combination of several lexical and morphological characteristics, to build a language model and a set of features for Machine Learning algorithms. We provide a detailed study on the proposed method performance and failure cases, indicating that it is competitive with state-of-the-art approaches on Legal Information Retrieval and Question Answering, while not needing extensive training data nor depending on expert produced knowledge. The proposed method achieved significant results in the competition, indicating a substantial level of adequacy for the tasks addressed.

Keywords

Relevant Article Civil Code Legal Text Relevance Analysis Lexical Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is supported partly by the grant of NII Research Cooperation and JAIST’s Research grant.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Danilo S. Carvalho
    • 1
    Email author
  • Minh-Tien Nguyen
    • 1
    • 2
  • Chien-Xuan Tran
    • 1
  • Minh-Le Nguyen
    • 1
  1. 1.School of Information ScienceJapan Advanced Institute of Science and Technology (JAIST)NomiJapan
  2. 2.Hung Yen University of Education and Technology (UTEHY)Hung YenVietnam

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