Results 171 to 180 of about 11,682 (203)
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Social Book Search with Pseudo-Relevance Feedback
2014Massive books with social information, e.g. reviews, rates and tags, have emerged in large numbers on the web. However, there are several limitations in traditional search methods for social books, as social books include complicated and various social information.
Bin Geng +5 more
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Pseudo-Relevance Feedback Driven for XML Query Expansion [PDF]
Journal of Convergence Information Technology, 2010 Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval performance.
Minjuan Zhong, Changxuan Wan
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Weighting visual features with pseudo relevance feedback for CBIR
Proceedings of the ACM International Conference on Image and Video Retrieval, 2010This paper proposes an automatic visual feature weighting method to enhance content-based image retrieval (CBIR). In particular, the proposed method is able to capture user's search intention by identifying the important visual features located at region of interest.
Jian Chen, Rui Ma, Zhong Su
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Evaluation of Pseudo-Relevance Feedback using Wikipedia
Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval, 2019Users have specific information needs which are expressed in short queries to information retrieval systems. The queries are unstructured, and they tend to be short and ambiguous in most cases. Using the shallow language statistics including probabilistic or language models such as BM25 or Indri respectively can enhance the retrieval system metrics ...
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Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, 2008Pseudo-relevance feedback assumes that most frequent terms in the pseudo-feedback documents are useful for the retrieval. In this study, we re-examine this assumption 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.
Guihong Cao +3 more
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Temporal Pseudo-relevance Feedback in Microblog Retrieval
2012Twitter has become a major outlet for news, discussion and commentary of on-going events and trends. Effective searching of Twitter collections poses a number of issues for traditional document-based information retrieval (IR) approaches, such as limited document term statistics and spam.
Stewart Whiting +2 more
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Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018Axiomatic analysis is a well-defined theoretical framework for analytical evaluation of information retrieval models. The current studies in axiomatic analysis implicitly assume that the constraints (axioms) are independent. In this paper, we revisit this assumption and hypothesize that there might be interdependence relationships between the existing ...
Ali Montazeralghaem +2 more
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Pseudo-Relevance Feedback for Multimedia Retrieval
2003Video information retrieval requires a system to find information rele-vant to a query which may be presented simultaneously in different ways through a text description, audio, still images and/or video sequences. The actual search also takes place in the text, audio, image or video domain.
Rong Yan +2 more
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Effective pseudo-relevance feedback for spoken document retrieval
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013With the exponential proliferation of multimedia associated with spoken documents, research on spoken document retrieval (SDR) has emerged and attracted much attention in the past two decades. Apart from much effort devoted to developing robust indexing and modeling techniques for representing spoken documents, a recent line of thought targets at the ...
Yi-Wen Chen +3 more
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Learning-Based Pseudo-Relevance Feedback for Patent Retrieval
2012Pseudo-relevance feedback (PRF) is an effective approach in Information Retrieval but unfortunately many experiments have shown that PRF is ineffective in patent retrieval. This is because the quality of initial results in the patent retrieval is poor and therefore estimating a relevance model via PRF often hurts the retrieval performance due to off ...
Parvaz Mahdabi, Fabio Crestani
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