Lexical-Morphological Modeling for Legal Text Analysis

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 co...

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Published in:arXiv.org 2016-09
Main Authors: Carvalho, Danilo S, Minh-Tien Nguyen, Tran, Xuan Chien, Minh Le Nguyen
Format: Article
Language:English
Subjects:
Adequacy
Algorithms
Competition
Information retrieval
Legal information
Machine learning
Morphology
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Minh-Tien Nguyen
Tran, Xuan Chien
Minh Le Nguyen
description 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.
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subjects Adequacy
Algorithms
Competition
Information retrieval
Legal information
Machine learning
Morphology
title Lexical-Morphological Modeling for Legal Text Analysis
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