Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities

TL;DR

This paper proposes a two-stage legal information retrieval method combining lexical and distributional techniques, with disambiguation rules, showing small but meaningful improvements in retrieval performance.

Contribution

It introduces a novel combination of lexical statistics and distributional sentence representations with disambiguation rules for legal IR.

Findings

Small gains in retrieval performance achieved

Disambiguation improves result reliability

Error analysis provides insights into method limitations

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.