Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities
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.
Cite
@article{arxiv.1706.01038,
title = {Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities},
author = {Danilo S. Carvalho and Duc-Vu Tran and Van-Khanh Tran and Le-Nguyen Minh},
journal= {arXiv preprint arXiv:1706.01038},
year = {2017}
}
Comments
wrong version