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Advances in Domain Independent Linear Text Segmentation
, 2000
"... This paper describes a method for linear text seg mc. ntation which is twice as accurate and over seven times as fast as the stateoftheart (Reynar, 1998). Intersentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering. ..."
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Cited by 186 (1 self)
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This paper describes a method for linear text seg mc. ntation which is twice as accurate and over seven times as fast as the stateoftheart (Reynar, 1998). Intersentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering.
Linear Text Segmentation: Approaches, Advances, and Applications
 Proceedings of CLUK3
, 2000
"... This paper presents a new algorithm for domain independent linear text segmentation which is twice as accurate and over seven times as fast as the stateoftheart [22]. The algorithm and statistical summarisation techniques were applied to a practical problem, improving document navigation for the ..."
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Cited by 2 (0 self)
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This paper presents a new algorithm for domain independent linear text segmentation which is twice as accurate and over seven times as fast as the stateoftheart [22]. The algorithm and statistical summarisation techniques were applied to a practical problem, improving document navigation
Image denoising using a scale mixture of Gaussians in the wavelet domain
 IEEE TRANS IMAGE PROCESSING
, 2003
"... We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vecto ..."
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Cited by 513 (17 self)
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We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian
MultiParagraph Segmentation of Expository Text
, 1994
"... This paper describes TextTiling, an algorithm for partitioning expository texts into coherent multiparagraph discourse units which reflect the subtopic structure of the texts. The algorithm uses domainindependent lexical frequency and distribution information to recognize the interactions of multi ..."
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Cited by 368 (11 self)
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This paper describes TextTiling, an algorithm for partitioning expository texts into coherent multiparagraph discourse units which reflect the subtopic structure of the texts. The algorithm uses domainindependent lexical frequency and distribution information to recognize the interactions
The use of MMR, diversitybased reranking for reordering documents and producing summaries
 In SIGIR
, 1998
"... jadeQcs.cmu.edu Abstract This paper presents a method for combining queryrelevance with informationnovelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in reranking retrieved docum ..."
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Cited by 768 (14 self)
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relevance to the user’s query. In contrast, we motivated the need for “relevant novelty ” as a potentially superior criterion. A first approximation to measuring relevant novelty is to measure relevance and novelty independently and provide a linear combination as the metric. We call the linear combination
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"... Advances in domain independent linear text segmentation This paper describes a method for linear text segmentation which is twice as accurate and over seven times as fast as the stateoftheart (Reynar, 1998). Intersentence similarity is replaced by rank in the local context. Boundary locations a ..."
Abstract
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Advances in domain independent linear text segmentation This paper describes a method for linear text segmentation which is twice as accurate and over seven times as fast as the stateoftheart (Reynar, 1998). Intersentence similarity is replaced by rank in the local context. Boundary locations
Systematic Nonlinear Planning
 In Proceedings of the Ninth National Conference on Artificial Intelligence
, 1991
"... This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly ..."
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Cited by 449 (3 self)
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This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly
SPADE: An efficient algorithm for mining frequent sequences
 Machine Learning
, 2001
"... Abstract. In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original proble ..."
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Cited by 437 (16 self)
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, and by an order of magnitude with some preprocessed data. It also has linear scalability with respect to the number of inputsequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.
Web Document Clustering: A Feasibility Demonstration
, 1998
"... Abstract Users of Web search engines are often forced to sift through the long ordered list of document “snippets” returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on the major s ..."
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Cited by 435 (3 self)
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that clusters based on snippets are almost as good as clusters created using the full text of Web documents. To satisfy the stringent requirements of the Web domain, we introduce an incremental, linear time (in the document collection size) algorithm called Suffix Tree Clustering (STC). which creates clusters
Statistical Models for Text Segmentation
 Machine Learning
, 1999
"... . This paper introduces a new statistical approach to automatically partitioning text into coherent segments. The approach is based on a technique that incrementally builds an exponential model to extract features that are correlated with the presence of boundaries in labeled training text. The mode ..."
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Cited by 273 (2 self)
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. This paper introduces a new statistical approach to automatically partitioning text into coherent segments. The approach is based on a technique that incrementally builds an exponential model to extract features that are correlated with the presence of boundaries in labeled training text
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