Abstract
This work describes a Shallow Question Answering System (QAS) restricted to legal documents. This system returns a set of relevant articles extracted from several regulation documents. The set of relevant articles allows inferring answers to questions posed in natural language. We take the approach of representing the set of all the articles as a graph; the question is split in two parts (called A and B), and each of them is added as part of the graph. Then several paths are constructed from part A of the question to part B, so that the shortest path contains the relevant articles to the question. We evaluate our method comparing the answers given by a traditional information retrieval system—vector space model adjusted for article retrieval, instead of document retrieval—and the answers to 21 questions given manually by the general lawyer of the National Polytechnic Institute, based on 26 different regulations (academy regulation, scholarships regulation, postgraduate studies regulation, etc.); with the answer of our system based on the same set of regulations. The results show that our system performs twice as better with regard to the traditional Information Retrieval model for Question Answering.
Work done under partial support of Mexican Government: SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN.
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Monroy, A., Calvo, H., Gelbukh, A. (2008). Using Graphs for Shallow Question Answering on Legal Documents. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_15
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DOI: https://doi.org/10.1007/978-3-540-88636-5_15
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