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
Query expansion using heterogeneous thesauri
Information Processing and Management: an International Journal
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Using Graphs for Shallow Question Answering on Legal Documents
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Evaluation of a thesaurus-based query expansion technique
PROPOR'03 Proceedings of the 6th international conference on Computational processing of the Portuguese language
CICLing'05 Proceedings of the 6th 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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Previous work has shown that modeling relationships between articles of a regulation as vertices of a graph network works twice as better than traditional information retrieval systems for returning articles relevant to the question. In this work we experiment by using natural language techniques such as lemmatizing and using manual and automatic thesauri for improving question based document retrieval. For the construction of the graph, we follow the approach of representing the set of all the articles as a graph; the question is split in two parts, 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 25 different regulations (academy regulation, scholarships regulation, postgraduate studies regulation, etc.); with the answer of our system based on the same set of regulations. We found that lemmatizing increases performance in around 10%, while the use of thesaurus has a low impact.