Traditional retrieval systems are hardly adequate for Legal Research, mainly because only returning the documents related to a given query is usually insufficient. Legal documents are extensive, and we posit that generating questions about them and detecting the answers provided by these documents help the Legal Research journey. This paper presents a pipeline that relates Legal Questions with documents answering them. We align features generated by Large Language Models with traditional clustering methods to find convergent and divergent answers to the same legal matter. We performed a case study with 50 legal documents on the Brazilian judiciary system. Our pipeline found convergent and divergent answers to 23 major legal questions regarding the case law for daily fines in Civil Procedural Law. The pipeline manual evaluation shows it managed to group diverse similar answers to the same question with an average precision of 0.85. It also managed to detect two divergent legal matters with an average F1 Score of 0.94.

Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), which allows the automatic creation of new datasets by taking an alignment between a text corpus and a Knowledge Base (KB). KBs can sometimes also provide additional information to the RE task. One of the methods that adopt this strategy is the RESIDE model, which proposes a distantly-supervised neural relation extraction using side information from KBs. Considering that this method outperformed state-of-the-art baselines, in this paper, we propose a related approach to RESIDE also using additional side information, but simplifying the sentence encoding with BERT embeddings. Through experiments, we show the effectiveness of the proposed method in Google Distant Supervision and Riedel datasets concerning the BGWA and RESIDE baseline methods. Although Area Under the Curve is decreased because of unbalanced datasets, P@N results have shown that the use of BERT as sentence encoding allows superior performance to baseline methods.