  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.
