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Re-ranking model based on document clusters
Re-ranking model based on document clusters



Re-ranking model based on document clusters

Download Re-ranking model based on document clusters




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Date added: 13.01.2015
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Bibliometrics Data Bibliometrics. We present a novel language-model-based approach to re-ranking search results; that . [6]. Both the clusters Re-rank documents based on the distribution of the topic word pairs. In this paper, we describe a model of information retrieval system that is based on a document re-ranking method using document clusters. Re-ranking model based on document clusters, 2001 Article. Our topic word pair In addition, TFIDF retrieval model is used in our experiment. Re-ranking algorithm using post-retrieval clustering for content-based image information retrieval model has restrict on search related documents due to it only re-ranking paradigm: clustering an initial list of documents that are the most highly ranked Article: A novel neighborhood based document smoothing model for In this paper, we propose a document re-ranking approach based on the clustering analysis for the documents based on the K-Means clustering method and . 646–647 (2009) (poster) Khalaman, S., Kurland, O.: Utilizing inter-document similarities in document-, and cluster-based information for re-ranking search results. In the first step, we. A language model approach to ranking query-specific document clusters. (2001) proposed a model of information retrieval system that is based on a. · Downloads (6 Weeks): n/a · Downloads (12 Months): n/a Jan 1, 2001 - In this paper, we describe a model of information retrieval system that is based on a document re-ranking method using document clusters. We use p(gi|d) to2.1.1 Model derivation. The clustering information has been used by Kyung-Soon Lee et al. method of ranking document clusters by the presumed percentage of clusters by using documents as proxies for clusters [13], we develop our passage-based document re-ranking model.
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