Abstract
Abstract The Legal Clinics (LC) are no-profit programs aimed to provide legal assistance to low-income citizens. Given the large number of consultations requested by the LC, the processes usually are delayed. This delay entails higher costs for the institutions that offer this legal advice. This paper explores the low-cost automation of legal problem identification in LC through the use of Natural Language Processing (NLP) techniques. We propose a methodology of text processing for the legal issue identification in the legal complaint assistance in LC. The method is based on preprocessing of the text, word to vector transformation, text identification models, and model evaluation for final classification. This methodology looks to accelerate the first step of the legal consultation process identifying the legal issue described by the LC user. The method is evaluated using real cases from the LC of the Santo Tomas University of Colombia. The results provided by the methodology depict a performance of around 95% for the legal issue identification. It is expected that this system will contribute to the delay decrease in the legal advice from LC, and it will help increase the number of advised users through virtual legal assistance.
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