Foundations of statistical natural language processing
Foundations of statistical natural language processing
A collaborative legal information retrieval system using dynamic logic programming
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
Natural language question answering: the view from here
Natural Language Engineering
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A question-answering system for Portuguese juridical documents
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Random walks on text structures
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
NLP for Shallow Question Answering of Legal Documents Using Graphs
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Link analysis for representing and retrieving legal information
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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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.