Abstract
In this paper we present our approach towards legal citation classification using incremental knowledge acquisition. This forms a part of our more ambitious goal of automatic legal text summarization. We created a large training and test corpus from court decision reports in Australia. We showed that, within less than a week, it is possible to develop a good quality knowledge base which considerably outperforms a baseline Machine Learning approach. We note that the problem of legal citation classification allows the use of Machine Learning as classified training data is available. For other subproblems of legal text summarization this is unlikely to be the case.
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References
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Galgani, F., Hoffmann, A. (2010). LEXA: Towards Automatic Legal Citation Classification. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_45
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DOI: https://doi.org/10.1007/978-3-642-17432-2_45
Publisher Name: Springer, Berlin, Heidelberg
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