Lexical-Morphological Modeling for Legal Text Analysis
Danilo S. Carvalho, Minh-Tien Nguyen, Chien-Xuan Tran, Le-Minh Nguyen · Lecture notes in computer science · 2017
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.