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47
Cluster-based retrieval using language models
- In Proceedings of SIGIR
, 2004
"... Previous research on cluster-based retrieval has been inconclusive as to whether it does bring improved retrieval effectiveness over document-based retrieval. Recent developments in the language modeling approach to IR have motivated us to re-examine this problem within this new retrieval framework. ..."
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Cited by 170 (13 self)
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Previous research on cluster-based retrieval has been inconclusive as to whether it does bring improved retrieval effectiveness over document-based retrieval. Recent developments in the language modeling approach to IR have motivated us to re-examine this problem within this new retrieval framework. We propose two new models for cluster-based retrieval and evaluate them on several TREC collections. We show that cluster-based retrieval can perform consistently across collections of realistic size, and significant improvements over document-based retrieval can be obtained in a fully automatic manner and without relevance information provided by human.
Fast and Intuitive Clustering of Web Documents
- In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
, 1997
"... Conventional document retrieval systems (e.g., Alta Vista) return long lists of ranked documents in response to user queries. Recently, document clustering has been put forth as an alternative method of organizing retrieval results (Cutting et al. 1992). A person browsing the clusters can discover ..."
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Cited by 114 (2 self)
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Conventional document retrieval systems (e.g., Alta Vista) return long lists of ranked documents in response to user queries. Recently, document clustering has been put forth as an alternative method of organizing retrieval results (Cutting et al. 1992). A person browsing the clusters can discover patterns that could be overlooked in the traditional presentation. This paper describes two novel clustering methods that intersect the documents in a cluster to determine the set of words (or phrases) shared by all the documents in the cluster. We report on experiments that evaluate these intersectionbased clustering methods on collections of snippets returned from Web search engines. First, we show that word-intersection clustering produces superior clusters and does so faster than standard techniques. Second, we show that our O(n log n) time phrase-intersection clustering method produces comparable clusters and does so more than two orders of magnitude faster than all methods tested. I...
The effectiveness of query-specific hierarchic clustering
- in information retrieval. Information Processing and Management
, 2002
"... Hierarchic document clustering has been widely applied to Information Retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search. However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view tha ..."
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Cited by 66 (3 self)
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Hierarchic document clustering has been widely applied to Information Retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search. However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view that if hierarchic clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional inverted file search. We conducted a number of experiments using five document collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering is indeed higher, and suggest that there is scope for its application to IR.
A methodology for clustering XML documents by structure
- Information Systems
, 2006
"... The processing and management of XML data are popular research issues. However, operations based on the structure of XML data have not received strong attention. These operations involve, among others, the grouping of structurally similar XML documents. Such grouping results from the application of ..."
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Cited by 50 (0 self)
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The processing and management of XML data are popular research issues. However, operations based on the structure of XML data have not received strong attention. These operations involve, among others, the grouping of structurally similar XML documents. Such grouping results from the application of clustering methods with distances that estimate the similarity between tree structures. This paper presents a framework for clustering XML documents by structure. Modeling the XML documents as rooted ordered labeled trees, we study the usage of structural distance metrics in hierarchical clustering algorithms to detect groups of structurally similar XML documents. We suggest the usage of structural summaries for trees to improve the performance of the distance calculation and at the same time to maintain or even improve its quality. Our approach is tested using a prototype testbed.
Anti-Aliasing on the Web
, 2004
"... It is increasingly common for users to interact with the web using a number of di#erent aliases. This trend is a doubleedged sword. On one hand, it is a fundamental building block in approaches to online privacy. On the other hand, there are economic and social consequences to allowing each user an ..."
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Cited by 31 (4 self)
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It is increasingly common for users to interact with the web using a number of di#erent aliases. This trend is a doubleedged sword. On one hand, it is a fundamental building block in approaches to online privacy. On the other hand, there are economic and social consequences to allowing each user an arbitrary number of free aliases. Thus, there is great interest in understanding the fundamental issues in obscuring the identities behind aliases.
Order-Theoretical Ranking
- JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCES (JASIS
, 2000
"... Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretic ..."
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Cited by 29 (5 self)
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Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretical and practical limitations. We present an approach to document ranking that explicitly addresses the word mismatch problem by exploiting interdocument similarity information in a novel way. Document ranking is seen as a querydocument transformation driven by a conceptual representation of the whole document collection, into which the query is merged. Our approach is based on the theory of concept (or Galois) lattices, which, we argue, provides a powerful, well-founded, and computationallytractable framework to model the space in which documents and query are represented and to compute such a transformation. We compared information retrieval using concept lattice-based ranking (CLR) to BMR and HCR. The results showed that HCR was outperformed by CLR as well as by BMR, and suggested that, of the two best methods, BMR achieved better performance than CLR on the whole document set while CLR compared more favorably when only the first retrieved documents were used for evaluation. We also evaluated the three methods' specific ability to rank documents that did not match the query, in which case the superiority of CLR over BMR and HCR (and that of HCR over BMR) was apparent.
On-Line New Event Detection, Clustering, And Tracking
, 1999
"... In this work, we discuss and evaluate solutions to text classification problems associated with the events that are reported in on-line sources of news. We present solutions to three related classification problems: new event detection, event clustering, and event tracking. The primary focus of this ..."
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Cited by 29 (0 self)
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In this work, we discuss and evaluate solutions to text classification problems associated with the events that are reported in on-line sources of news. We present solutions to three related classification problems: new event detection, event clustering, and event tracking. The primary focus of this thesis is new event detection, where the goal is to identify news stories that have not previously been reported, in a stream of broadcast news comprising radio, television, and newswire. We present an algorithm for new event detection, and analyze the effects of incorporating domain properties into the classification algorithm. We explore a solution that models the temporal relationship between news stories, and investigate the use of proper noun phrase
