FIRE2019@AILA: Legal Information Retrieval Using Improved BM25

This paper details the approaches of implementing the tasks of identifying relevant precedents and identifying relevant statues in the evaluation of Artificial Intelligence for Legal Assistance proposed by Forum of Information Retrieval Evaluation in 2019(AILA@Fire2019). We formalize the two tasks as the issue of information retrieval, and present the improved BM25 models to retrieve the prior cases and identify the relevant statues. For the task of identifying relevant precedents, the proposed improved BM25 model integrates the relevance scores of the original current case and the filtered current case. For the task of identifying relevant statues, the proposed improved BM25 models exploit the search results as the reference documents of the current case and integrate the ranking information of search results into the BM25 model. Comparisons to the other submissions for the same tasks, our improved BM25 model achieves the top performers for the task of identifying relevant precedents on all evaluation measures. For the task of identifying relevant statues, the improved BM25 model wins the second place on 1/rank of first relevant document and the third place on BPREF.