Stuctured Queries for Legal Search.
Yangbo Zhu, Le Zhao, Jamie Callan, Jaime Carbonell · Text REtrieval Conference · 2007
This paper reports the experiments of using Indri for the main and routing (relevance feedback) tasks in the TREC 2007 Legal Track. For the main task, we analyze ranking algorithms using different fields, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing task, we train a linear SVM classifier for each topic. Terms with the largest weights are selected to form new queries. Both keywords and simple structured features (term.field) have been investigated. Named-Entity tags, LingPipe sentence breaker and metadata fields of the original documents are used to generate the field information. Results show that structured features and weighted queries improves retrieval, but only marginally. We also show which structures are more useful. It turns out metadata fields are not as important as we thought.