Judging the Spatial Relevance of Documents for GIR
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Relevance Feedback
Relevance feedback techniques are important to information retrieval (IR), which can effectively improve the performance of IR. They have been proved by many existing work. The feedback includes positive and negative relevance one. The most of the previous work using feedback have focused on positive relevance feedback and pseudo relevance feedback in IR. In recent years, some work has been done and investigated the negative relevance feedback in IR. However, this paper highlights the incorporation or integration between the language models based positive and negative relevance feedback in IR, where both types of feedback are used to modify and expand the user's query model. Our experimental results of using several TREC collections show that this method is significantly outperform the relevance feedback and pseudo relevance feedback in terms of the retrieval accuracy.
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Relevance feedback, which modifies queries using judgements of the relevance of a few, highly-ranked documents, has historically been an important method for increasing the performance of information retrieval systems. In this paper, we extend the inference network model introduced by Turtle and Croft to include relevance feedback techniques. The difference between relevance feedback on text abstracts and full text collections is studied. Preliminary results for relevance feedback on the structured queries supported by the inference net model are also reported.
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We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by using relevant and non-relevant information separately. Empirical exploration attests to the merits of our methods, and sheds light on the effectiveness of using and integrating relevance feedback for textual units of varying granularities.
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Relevance feedback techniques have been success fully applied to document retrieval systems (DRS) to refine queries and document representations. This paper represents a first attempt to apply and optimise relevance feedback techniques to improve retrieval in a text-based image archival system. The design exploits the rapid assessment possible with image data to facilitate query refinement and collect large amounts of relevance feedback data This data can then be used to extend incomplete image descriptions, thus ameliorat ing problems associated with text annotation of images. Visu ally oriented relevance feedback and query modification is implemented using direct mampulation of icons. An algorithm designed for dynamic modification of image descriptions based on relevance feedback is proposed, implemented, and experi mentally tested. Initial experiments show significant improve ments and demonstrate the potential of using these tech niques for image retrieval applications.
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The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs. Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper, it is proposed to perform relevance feedback on keyword space. The relevance feedback is supposed to work with interactive keyword map system, which visualizes the relationship between keywords extracted from retrieved results. As the first step for realizing relevance feedback based on interactive keyword map, this paper also proposes the algorithm for extracting the pair of keywords that reflects a userpsilas interest from the keyword map. Experimental results are given for showing how the algorithm works on the keyword map that is modified by the user.
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Relevance feedback techniques are important to Information retrieval (IR), which can effectively improve the performance of IR. The feedback includes positive and negative relevance one. The most of the previous work using feedback have focused on positive relevance feedback and pseudo relevance feedback in IR. In recent years, some work has been done and investigated the negative relevance feedback in IR. However, this paper highlights the incorporation or integration between the language models based positive and negative relevance feedback in IR, and through positive and negative feedback documents proportion on queries classification, with different parameters adjustment of positive and negative feedback ratio, where both types of feedback are used to modify and expand the user's query model. Our experimental results of using several TREC collections show that this method is significantly outperform the relevance feedback and pseudo relevance feedback in terms of the retrieval accuracy.
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Positive feedback
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Relevance feedback is one of the strong components of Surfimage, the INRIA content-based image retrieval system. Relevance feedback is about learning from user interaction, and is useful in tasks like query refinement and multiple queries. We present two relevance feedback techniques currently implemented in Surfimage.
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We describe the participation of the University of Amsterdam’s Intelligent Systems Lab in the relevance feedback track at TREC 2009. Our main conclusion for the relevance feedback track is that a topical diversity approach provides good feedback documents. Further, we find that our relevance feedback algorithm seems to help most when there are sufficient relevant documents available.
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The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs.Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper, it is proposed to perform relevance feedback on keyword space. The relevance feedback is supposed to work with interactive keyword map system, which visualizes the relationship between keywords extracted from retrieved results. As the first step for realizing relevance feedback based on interactive keyword map, this paper also proposes the algorithm for extracting the pair of keywords that reflects a userpsilas interest from the keyword map. Experimental results are given for showing how the algorithm works on the keyword map that is modified by the user.
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This paper addresses relevance feedback as an alternative to keyword-based search engines for sifting through large PDF document collections and extracting the most relevant documents (especially for literature review purposes). Until now, relevance feedback has only been used in content-based image and video retrieval due to the inability to query those media types without keywords. Since PDF journal articles contain many valuable non-keyword features such as structure and formatting information as well as embedded figures, they would benefit from relevance feedback. Stripping a PDF into "full-text" for indexing purposes disregards these important features. We discuss how they can be used to our advantage and look to integrate the wealth of knowledge from relevance feedback text-based information retrieval. We argue for the benefits of placing the burden of relevance judgement on the user rather than the retrieval system and present alternative document views that quickly allow the user to deem relevance.
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