Abstract

Information retrieval algorithms leverage various collection statistics to improve performance. Because these statistics are often computed on a relatively small evaluation corpus, we believe using larger, non-evaluation corpora should improve performance. Specifically, we advocate incorporating external corpora based on language modeling. We refer to this process as external expansion. When compared to traditional pseudo-relevance feedback techniques, external expansion is more stable across topics and up to 10% more e#ective in terms of mean average precision. Our results show that using a high quality corpus that is comparable to the evaluation corpus can be as, if not more, e#ective than using the web. Our results also show that external expansion outperforms simulated relevance feedback. In addition, we propose a method for predicting the extent to which external expansion will improve retrieval performance. Our new measure demonstrates positive correlation with improvements in mean average precision.

Citations