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Re-ranking search results using language models of query-specific clusters

Oren Kurland
2008 Information retrieval (Boston)  
Keywords Query-specific clusters Á Cluster-based language models Á Cluster-based re-ranking Á Cluster-based smoothing  ...  We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent clusters for ranking all documents in a corpus, to the re-ranking setting wherein  ...  The paper is based upon work supported in part by the National Science Foundation under grant no. IIS-0329064 and by a research award from Google.  ... 
doi:10.1007/s10791-008-9065-9 fatcat:eq3ey5fgibaj5h72nqofrezzeu

Integrating clusters created offline with query-specific clusters for document retrieval

Lior Meister, Oren Kurland, Inna Gelfer Kalmanovich
2009 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09  
Previous work on cluster-based document retrieval has used either static document clusters that are created offline, or query-specific (dynamic) document clusters that are created from top-retrieved documents  ...  We present the potential merit of integrating these two types of clusters.  ...  The paper is based upon work supported in part by IBM's and Google's faculty research awards.  ... 
doi:10.1145/1571941.1572088 dblp:conf/sigir/MeisterKK09 fatcat:zzyknmmk35babmllmb6kqeeaye

Re-ranking search results using an additional retrieved list

Lior Meister, Oren Kurland, Inna Gelfer Kalmanovich
2010 Information retrieval (Boston)  
Furthermore, we show that our methods can help to effectively tackle two long-standing challenges; namely, integration of document-based and cluster-based retrieved results; and, improvement of the performance  ...  We present a novel approach to re-ranking a list that was retrieved in response to a query so as to improve precision at the very top ranks.  ...  Furthermore, our SimRank algorithm is reminiscent of a cluster-based re-ranking model wherein similarities between documents in the list and clusters of documents in the list are utilized [29] .  ... 
doi:10.1007/s10791-010-9150-8 fatcat:f3tpdowtofge7cnwtcuvvh4nsa

Query-performance prediction and cluster ranking

Oren Kurland, Fiana Raiber, Anna Shtok
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
The second task is cluster ranking, that is, ranking clusters of similar documents by their presumed effectiveness (i.e., relevance) with respect to the query.  ...  Furthermore, we show that several state-of-the-art methods that were independently devised for each of the two tasks are based on the same principles.  ...  Clusters were ranked based on the mean retrieval score of their constituent documents [23, 13, 25] .  ... 
doi:10.1145/2396761.2398666 dblp:conf/cikm/KurlandRS12 fatcat:mcoc43qfjnaq5p2sqzrbajnmfe

Using Vagueness Measures to Re-rank Documents Retrieved by a Fuzzy Set Information Retrieval Model

Stephen Lynn, Yiu-Kai Ng
2008 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery  
Traditional information retrieval (IR) systems evaluate user queries and retrieve/rank documents based on matching keywords in user queries with words in documents.  ...  based on the word senses as defined in WordNet.  ...  Based on the re-ranked value of each document, the reranking of the initial result set is shown in Table 2 .  ... 
doi:10.1109/fskd.2008.546 dblp:conf/fskd/LynnN08 fatcat:ylx5j2susnfatbgw34nh7mlvfm

The Opposite of Smoothing: A Language Model Approach to Ranking Query-Specific Document Clusters

O. Kurland, E. Krikon
2011 The Journal of Artificial Intelligence Research  
While most previous cluster ranking approaches focus on the cluster as a whole, our model utilizes also information induced from documents associated with the cluster.  ...  We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain.  ...  We also thank Lillian Lee for helpful comments on the work presented in this paper, and for discussions that led to ideas presented here; specifically, the cluster-centrality induction method is a fruit  ... 
doi:10.1613/jair.3327 fatcat:fdtog7uqlfhszgzx2mh6zbykfm

Research on Expert Search at Enterprise Track of TREC 2005

Yunbo Cao, Jingjing Liu, Shenghua Bao, Hang Li
2005 Text Retrieval Conference  
CLUSTERING-BASED RE-RANKING In the clustering-based re-ranking, we try to utilize relations between people to enhance expert search results. The relationships we use belong to two categories: 1.  ...  Clustering-based re-ranking 7 Average Precision Bpref Relevant Retr@10 Baseline 0.6036 0.9505 6.20 Baseline + re-ranking 0.6253 0.9505 6.20 Table 8 .  ... 
dblp:conf/trec/CaoLBL05 fatcat:3cbyhmwsqzhfbhaxhkwsdawvki

A Multiple-Stage Approach to Re-ranking Medical Documents [chapter]

Heung-Seon Oh, Yuchul Jung, Kwang-Young Kim
2015 Lecture Notes in Computer Science  
The key idea of our method is to compute accurate similarity scores via multiple stages of re-ranking documents from initial documents retrieved by a search engine.  ...  The task is to retrieve relevant documents from a medical collection given a query generated from a discharge summary.  ...  Based on the initial documents, re-ranking is performed via multiple stages.  ... 
doi:10.1007/978-3-319-24027-5_14 fatcat:e2mfiwzpdreudnau6pfmrdoy6m

The cluster hypothesis in information retrieval

Oren Kurland
2013 Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13  
The cluster hypothesis • Historical view of the effect of the hypothesis on work on ad hoc information retrieval • Testing the cluster hypothesis • Cluster-based document retrieval • Using topic models  ...  to the information need expressed by a query • Vector space model • Probabilistic approaches • Language modeling framework • Divergence from randomness framework • Learning to rank The cluster hypothesis  ...  ranking is based on the initial document scores which were assigned in response to the query or on the similarity between the document and the cluster centroid • Jardine&Rijsbergen '71, Croft '80, Voorhees  ... 
doi:10.1145/2484028.2484192 dblp:conf/sigir/Kurland13 fatcat:3lw6kkfzs5gsvmsvkpwlynck2u

Document re-ranking using cluster validation and label propagation

Lingpeng Yang, Donghong Ji, Guodong Zhou, Yu Nie, Guozheng Xiao
2006 Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06  
For pseudo relevant documents, we determine a cluster of documents from the top ones via cluster validation-based kmeans clustering; for pseudo irrelevant ones, we pick a set of documents from the bottom  ...  This paper proposes a novel document re-ranking approach in information retrieval, which is done by a label propagationbased semi-supervised learning algorithm to utilize the intrinsic structure underlying  ...  For example, [1] re-ranked documents by using document distances to modify their relevance weights while [8] proposed their approach based on document clustering.  ... 
doi:10.1145/1183614.1183713 dblp:conf/cikm/YangJZNX06 fatcat:checcs6atrgulfrh23lxt4hz6m

Respect My Authority! HITS Without Hyperlinks, Utilizing Cluster-Based Language Models [article]

Oren Kurland, Lillian Lee
2008 arXiv   pre-print
The main idea is to perform re-ranking 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  ...  For example, authority-based re-ranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs.  ...  This paper is based upon work supported in part by the National Science Foundation under grant no. IIS-0329064 and an Alfred P. Sloan Research Fellowship.  ... 
arXiv:0804.3599v1 fatcat:3bcgrzziabawrfms2f3zhukwbq

Information Retrieval Using Label Propagation Based Ranking

Lingpeng Yang, Donghong Ji, Yu Nie
2007 NTCIR Conference on Evaluation of Information Access Technologies  
For pseudo relevant documents, we determine a cluster of documents from the top ones via cluster validation-based k-means clustering; for pseudo irrelevant ones, we pick a set of documents from the bottom  ...  Our document re-ranking method is done by a label propagation-based semi-supervised learning algorithm to utilize the intrinsic structure underlying in the large document data.  ...  In section 4, we describe our document re-ranking method based on cluster validation and label propagation. In section 5, we describe how to do query expansion in our system.  ... 
dblp:conf/ntcir/YangJN07 fatcat:5dkvfvh2pvdjfd53nxb6pmhxvm

Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results [article]

Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
2019 arXiv   pre-print
We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking models.  ...  In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results.  ...  re-ranking model.  ... 
arXiv:1912.04713v1 fatcat:lpa5xj3vbbcyfaew3anrm7cgza

Cluster-based fusion of retrieved lists

Anna Khudyak Kozorovitsky, Oren Kurland
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
The performance also transcends that of standard fusion of re-ranked lists, where list re-ranking is based on clusters created from documents in the list.  ...  Specifically, our model integrates information induced from clusters of similar documents created across the lists with that produced by some fusion method that relies on retrieval scores (ranks).  ...  This paper is based upon work supported in part by the Israel Science Foundation under grant no. 557/09, and by IBM's SUR award.  ... 
doi:10.1145/2009916.2010035 dblp:conf/sigir/KozorovitzkyK11 fatcat:6grtvoc6wvgttd6nedxbojysey

Corpus structure, language models, and ad hoc information retrieval [article]

Oren Kurland, Lillian Lee
2004 arXiv   pre-print
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.  ...  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  ...  This paper is based upon work supported in part by the National Science Foundation under grants ITR/IM IIS-0081334 and IIS-0329064 and by an Alfred P. Sloan Research Fellowship.  ... 
arXiv:cs/0405044v1 fatcat:mzindgrkarbnnnys54yxypdaxm
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