Query Expansion for Noisy Legal Documents

The vocabulary of the TREC Legal OCR collection is noisy and huge. Standard techniques for improving retrieval performance such as content-based query expansion are ineffective for such document collection. In our work, we focused on exploiting metadata using blind relevance feedback, iterative improvement from the reference Boolean run, and the effects of using terms from different topic fields for automatic query formulation. This paper describes our methodologies and results.