Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Formal multiple-bernoulli models for language modeling
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A study of Poisson query generation model for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A study of methods for negative relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Hypergeometric language models for republished article finding
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Lower-bounding term frequency normalization
Proceedings of the 20th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
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.