A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Better than the real thing?: iterative pseudo-query processing using cluster-based language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A new robust relevance model in the language model framework
Information Processing and Management: an International Journal
Proceedings of the 18th international conference on World wide web
One-class clustering in the text domain
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Enhancing relevance models with adaptive passage retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
| Hi-index | 0.00 |
Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve the retrieval accuracy. A serious problem is that the performance is often very sensitive to the number of pseudo feedback documents. In this poster, we address this problem in a language modeling framework. We propose a novel two-stage mixture model, which is less sensitive to the number of pseudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retrieval accuracy.