TY - GEN
T1 - Query likelihood with negative query generation
AU - Lv, Yuanhua
AU - Zhai, Chengxiang
PY - 2012
Y1 - 2012
N2 - The query likelihood retrieval function has proven to be empirically effective for many retrieval tasks. From theoretical perspective, however, the justification of the standard query likelihood retrieval function requires an unrealistic assumption that ignores the generation of a "negative query" from a document. This suggests that it is a potentially non-optimal retrieval function. In this paper, we attempt to improve the query likelihood function by bringing back the negative query generation. We propose an effective approach to estimate the probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. The proposed approach not only bridges the theoretical gap in the existing query likelihood retrieval function, but also improves retrieval effectiveness significantly with no additional computational cost.
AB - The query likelihood retrieval function has proven to be empirically effective for many retrieval tasks. From theoretical perspective, however, the justification of the standard query likelihood retrieval function requires an unrealistic assumption that ignores the generation of a "negative query" from a document. This suggests that it is a potentially non-optimal retrieval function. In this paper, we attempt to improve the query likelihood function by bringing back the negative query generation. We propose an effective approach to estimate the probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. The proposed approach not only bridges the theoretical gap in the existing query likelihood retrieval function, but also improves retrieval effectiveness significantly with no additional computational cost.
KW - language model
KW - negative query generation
KW - principle of maximum entropy
KW - probability ranking principle
KW - query likelihood
UR - http://www.scopus.com/inward/record.url?scp=84871060640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871060640&partnerID=8YFLogxK
U2 - 10.1145/2396761.2398520
DO - 10.1145/2396761.2398520
M3 - Conference contribution
AN - SCOPUS:84871060640
SN - 9781450311564
T3 - ACM International Conference Proceeding Series
SP - 1799
EP - 1803
BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Y2 - 29 October 2012 through 2 November 2012
ER -