Relevance feedback with too much data
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Effective ranking with arbitrary passages
Journal of the American Society for Information Science and Technology
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
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
Completely-arbitrary passage retrieval in language modeling approach
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
An improved feedback approach using relevant local posts for blog feed retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Utilizing local evidence for blog feed search
Information Retrieval
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
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Different from the traditional document-level feedback, passage-level feedback restricts the context of selecting relevant terms to a passage in a document, rather than to the entire document. It can thus avoid the selection of nonrelevant terms from non-relevant parts in a document. The most recent work of passage-level feedback has been investigated from the viewpoint of the fixed-window type of passage. However, the fixed-window type of passage has limitation in optimizing the passage-level feedback, since it includes a query-independent portion. To minimize the query-independence of the passage, this paper proposes a new type of passage, called completely-arbitrary passage. Based on this, we devise a novel two-stage passage feedback - which consists of passage-retrieval and passage-extension as sub-steps, unlike previous single-stage passage feedback relying only on passage retrieval. Experimental results show that the proposed two-stage passage-level feedback much significantly improves the document-level feedback than the single-stage passage feedback that uses the fixed-window type of passage.