TY - GEN
T1 - NLP for shallow question answering of legal documents using graphs
AU - Monroy, Alfredo
AU - Calvo, Hiramand
AU - Gelbukh, Alexander
N1 - Funding Information:
We thank the support of Mexican Government (SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN) and Japanese Government. Second author is a JSPS fellow.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=67650513845&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-00382-0_40
DO - 10.1007/978-3-642-00382-0_40
M3 - Contribución a la conferencia
SN - 3642003818
SN - 9783642003813
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 498
EP - 508
BT - Computational Linguistics and Intelligent Text Processing - 10th International Conference, CICLing 2009, Proceedings
T2 - 10th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2009
Y2 - 1 March 2009 through 7 March 2009
ER -