Is linguistic information relevant for the classification of legal texts?

• Autor(a) principal: Gonçalves, Teresa
• Data de Publicação: 2005
• Outros Autores: Quaresma, Paulo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/2561
• topic: Text classification

Text classification is an important task in the legal domain. In fact, most of the legal information is stored as text in a quite unstructured format and it is important to be able to automatically classify these texts into a predefined set of concepts. Support Vector Machines (SVM), a machine learning al- gorithm, has shown to be a good classifier for text bases [Joachims, 2002]. In this paper, SVMs are applied to the classification of European Portuguese legal texts – the Por- tuguese Attorney General’s Office Decisions – and the rele- vance of linguistic information in this domain, namely lem- matisation and part-of-speech tags, is evaluated. The obtained results show that some linguistic information (namely, lemmatisation and the part-of-speech tags) can be successfully used to improve the classification results and, simultaneously, to decrease the number of features needed by the learning algorithm.