Lexical-Morphological Modeling for Legal Text Analysis
This work addresses legal text analysis for information retrieval and question answering, but it is incremental as it builds on existing methods with specific feature combinations.
The authors tackled the problem of legal information extraction and entailment in the COLIEE competition by combining lexical and morphological features to build a language model and machine learning features, achieving competitive results with state-of-the-art approaches without needing extensive training data or expert knowledge.
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.