Retrieval of legal documents using query expansion
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Structure of query by a lawyer and a layman is different. Legal content in the layman’s query is very less. Thus pre-processing of these queries is required for better retrieval performance. In this thesis, we used various query expansion techniques and found that increasing query size increases system performance. MAP of 0.5034 was obtained by using BM25 retrieval model with query expansion up to 2550 terms using Bo1 query expansion model. By explicitly adding terms to the query, using topics obtained from topic modelling, a MAP value of 0.4281 was obtained. Further by relevance feedback of documents using topic modelling and only 2 cycles of feedback, we got MAP of 0.3832. Baseline result that we had was MAP value of 0.3799 using In_expC2 retrieval model. We also compared the relevance judgment of a lawyer and a non-lawyer and found out that for relative evaluation of two systems, non-lawyer’s relevance judgment is at par with the lawyer’s judgment.
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