Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Proximity-based rocchio's model for pseudo relevance
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Hi-index | 0.01 |
Pseudo-Relevance Feedback (PRF) assumes that the top-ranking n documents of the initial retrieval are relevant and extracts expansion terms from them. In this work, we introduce the notion of pseudo-irrelevant documents, i.e. high-scoring documents outside of top n that are highly unlikely to be relevant. We show how pseudo-irrelevant documents can be used to extract better expansion terms from the top-ranking n documents: good expansion terms are those which discriminate the top-ranking n documents from the pseudo-irrelevant documents. Our approach gives substantial improvements in retrieval performance over Model-based Feedback on several test collections.