Heliod at TREC Legal 2011: Learning to Rank from Relevance Feedback for e-Discovery

We present the results of applying a learning to rank algorithm to the 2011 TREC Legal dataset. The learning to rank algorithm we use was designed to maximize NDCG, MAP, and AUC scores. We therefore examine our results using the AUC and hypothetical F1 scores. We find query expansion and learning to rank improve scores beyond standard language model retrieval, however learning to rank does not outperform query expansion.