Knowledge Acquisition for Categorization of Legal Case Reports
Conference paper
Abstract
Natural language processing in complex domains, such as law, requires elaborate features, and their interaction is often difficult to model: thus traditional machine learning approaches might fail to perform satisfactorily. This paper describes our approach to assign categories and generate catchphrases for legal case reports. We describe our knowledge acquisition framework which lets us quickly build classification rules, using a small number of features, to assign general labels to cases. We show how the resulting knowledge base outperforms machine learning models which use both the designed features or a traditional bag of word representation. We also describe how to extend this approach to extract from the full text a list of more specific catchphrases that describe the content of the case.
Keywords
Machine Learning Knowledge Acquisition Machine Translation Legal Text Word Representation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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