Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities

14 pages•Published: June 3, 2017

Danilo S. Carvalho, Vu Tran, Khanh Van Tran and Nguyen Le Minh

Abstract

Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high efficiency Legal Information Retrieval (IR) and Question Answering (QA) methods. The IR task in particular has a set of unique challenges that invite the use of semantic motivated NLP techniques. In this work, a two-stage method for Legal Information Retrieval is proposed, combining lexical statistics and distributional sentence representations in the context of Competition on Legal Information Extraction/Entailment (COLIEE). The combination is done with the use of disambiguation rules, applied over the rankings obtained through n-gram statistics. After the ranking is done, its results are evaluated for ambiguity, and disambiguation is done if a result is decided to be unreliable for a given query. Competition and experimental results indicate small gains in overall retrieval performance using the proposed approach. Additionally, an analysis of error and improvement cases is presented for a better understanding of the contributions.

Keyphrases: distributional semantics, language modeling, legal information retrieval, term order probabilities

In: Ken Satoh, Mi-Young Kim, Yoshinobu Kano, Randy Goebel and Tiago Oliveira (editors). COLIEE 2017. 4th Competition on Legal Information Extraction and Entailment, vol 47, pages 43-56.

Links:	https://easychair.org/publications/paper/7Pv
	https://doi.org/10.29007/2xzw

BibTeX entry

@inproceedings{COLIEE2017:Improving_Legal_Information_Retrieval,
  author    = {Danilo S. Carvalho and Vu Tran and Khanh Van Tran and Nguyen Le Minh},
  title     = {Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities},
  booktitle = {COLIEE 2017. 4th Competition on Legal Information Extraction and Entailment},
  editor    = {Ken Satoh and Mi-Young Kim and Yoshinobu Kano and Randy Goebel and Tiago Oliveira},
  series    = {EPiC Series in Computing},
  volume    = {47},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/7Pv},
  doi       = {10.29007/2xzw},
  pages     = {43-56},
  year      = {2017}}

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