Lexical-Morphological Modeling for Legal Text Analysis

TL;DR

This paper introduces a lexical-morphological approach for legal text analysis that effectively retrieves relevant documents and determines textual entailment, competing well with state-of-the-art methods without extensive training data.

Contribution

It presents a novel combination of lexical and morphological features for legal text analysis that is efficient and does not rely heavily on large training datasets or expert knowledge.

Findings

Competitive with state-of-the-art approaches

Does not require extensive training data

Achieved significant results in COLIEE competition

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.