SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Smoothing Functions for Automatic Relevance Feedback in Information Retrieval
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Active feedback in ad hoc information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Term feedback for information retrieval with language models
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improve retrieval accuracy for difficult queries using negative feedback
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A split-list approach for relevance feedback in information retrieval
Information Processing and Management: an International Journal
Effectiveness of search result classification based on relevance feedback
Journal of Information Science
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In modern Information Retrieval, traditional relevance feedback techniques, which utilize the terms in the relevant documents to enrich the user's initial query, is an effective method to improve retrieval performance. In this paper, we re-examine this method and show that it does not hold in reality - many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. We then propose a Text Classification Based method for relevance feedback. The classifier trained on the feedback documents can classify the rest of the documents. Thus, in the result list, the relevant documents will be in front of the non-relevant documents. This new approach avoids modifying the query via text classification algorithm in the relevance feedback, and it is a new direction for the relevance feedback techniques. Our Experiments on TREC dataset demonstrate that retrieval effectiveness can be much improved when text classification is used.