Oren Kurland
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Publications by Oren Kurland (bibliography)
» 2008 «
Kurland, Oren (2008): The opposite of smoothing: a language model approach to ranking query-specific document clusters. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 171-178. Available online
“Exploiting information induced from (query-specific) clustering of top-retrieved documents has long been proposed as means for improving precision at the very top ranks of the returned results. We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain. While most previous cluster ranking approaches focus on the cluster as a whole, our model also exploits information induced from documents associated with the cluster. Our model substantially outperforms previous approaches for identifying clusters containing a high relevant-document percentage. Furthermore, using the model to produce document ranking yields precision-at-top-ranks performance that is consistently better than that of the initial ranking upon which clustering is performed; the performance also favorably compares with that of a state-of-the-art pseudo-feedback retrieval method.”
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Kurland, Oren and Domshlak, Carmel (2008): A rank-aggregation approach to searching for optimal query-specific clusters. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 547-554. Available online
“To improve the precision at the very top ranks of a document list presented in response to a query, researchers suggested to exploit information induced from clustering of documents highly ranked by some initial search. We propose a novel model for ranking such (query-specific) clusters by the presumed percentage of relevant documents that they contain. The model is based on (i) proposing a palette of "witness" cluster properties that purportedly correlate with this percentage, (ii) devising concrete quantitative measures for these properties, and (iii) ordering the clusters via aggregation of rankings induced by these individual measures. Empirical evaluation shows that our model is consistently more effective than previously suggested methods in detecting clusters containing a high relevant-document percentage. Furthermore, the precision-at-top-ranks performance of this model transcends that of standard document-based retrieval, and competes with that of a state-of-the-art document-based retrieval approach.”
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Zighelnic, Liron and Kurland, Oren (2008): Query-drift prevention for robust query expansion. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 825-826. Available online
“Pseudo-feedback-based automatic query expansion yields effective retrieval performance on average, but results in performance inferior to that of using the original query for many information needs. We address an important cause of this robustness issue, namely, the query drift problem, by fusing the results retrieved in response to the original query and to its expanded form. Our approach posts performance that is significantly better than that of retrieval based only on the original query and more robust than that of retrieval using the expanded query.”
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Bendersky, Michael and Kurland, Oren (2008): Re-ranking search results using document-passage graphs. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 853-854. Available online
“We present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ranks. While most work on passage-based document retrieval ranks a document based on the query similarity of its constituent passages, our approach leverages information about the centrality of the document passages with respect to the initial document list. Passage centrality is induced over a bipartite document-passage graph, wherein edge weights represent document-passage similarities. Empirical evaluation shows that our approach yields effective re-ranking performance. Furthermore, the performance is superior to that of previously proposed passage-based document ranking methods.”
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» 2007 «
Winaver, Mattan, Kurland, Oren and Domshlak, Carmel (2007): Towards robust query expansion: model selection in the language modeling framework. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 729-730. Available online
“We propose a language-model-based approach for addressing the performance robustness problem -- with respect to free-parameters' values -- of pseudo-feedback-based query-expansion methods. Given a query, we create a set of language models representing different forms of its expansion by varying the parameters' values of some expansion method; then, we select a single model using criteria originally proposed for evaluating the performance of using the original query, or for deciding whether to employ expansion at all. Experimental results show that these criteria are highly effective in selecting relevance language models that are not only significantly more effective than poor performing ones, but that also yield performance that is almost indistinguishable from that of manually optimized relevance models.”
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» 2006 «
Kurland, Oren and Lee, Lillian (2006): Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 83-90. Available online
“We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform reranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via consideration of language models induced from them. We find that our cluster-document graphs give rise to much better retrieval performance than previously proposed document-only graphs do. For example, authority-based reranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs. Moreover, we also show that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.”
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» 2005 «
Kurland, Oren, Lee, Lillian and Domshlak, Carmel (2005): Better than the real thing?: iterative pseudo-query processing using cluster-based language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 19-26. Available online
“We present a novel approach to pseudo-feedback-based ad hoc retrieval that uses language models induced from both documents and clusters. First, we treat the pseudo-feedback documents produced in response to the original query as a set of pseudo-query that themselves can serve as input to the retrieval process. Observing that the documents returned in response to the pseudo-query can then act as pseudo-query for subsequent rounds, we arrive at a formulation of pseudo-query-based retrieval as an iterative process. Experiments show that several concrete instantiations of this idea, when applied in conjunction with techniques designed to heighten precision, yield performance results rivaling those of a number of previously-proposed algorithms, including the standard language-modeling approach. The use of cluster-based language models is a key contributing factor to our algorithms' success.”
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Kurland, Oren and Lee, Lillian (2005): PageRank without hyperlinks: structural re-ranking using links induced by language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 306-313. Available online
“Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.”
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» 2004 «
Kurland, Oren and Lee, Lillian (2004): Corpus structure, language models, and ad hoc information retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 194-201. Available online
“Most previous work on the recently developed language-modeling approach to information retrieval focuses on document-specific characteristics, and therefore does not take into account the structure of the surrounding corpus. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in precision and recall, and our new interpolation algorithm posts statistically significant improvements for both metrics over all three corpora tested.”
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Mar 21st, 2010
Changes to this page (author)
21 Feb 2010: Enabled abstracts to be shown on Oren Kurland's author page.08 Apr 2009: Author was edited 08 Apr 2009: Author was edited
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