Sciweavers

Share
SIGIR
2006
ACM

Improving the estimation of relevance models using large external corpora

9 years 4 months ago
Improving the estimation of relevance models using large external corpora
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 effective 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, effective 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...
Fernando Diaz, Donald Metzler
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where SIGIR
Authors Fernando Diaz, Donald Metzler
Comments (0)
books