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Results 1 - 10
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2,486
Cumulated Gain-based Evaluation of IR Techniques
- ACM Transactions on Information Systems
, 2002
"... Modem large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to the users. In order to develop IR techniques to this direction, i ..."
Abstract
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Cited by 694 (3 self)
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, it is necessary to develop evaluation approaches and methods that credit IR methods for their ability to retrieve highly relevant documents. This can be done by extending traditional evaluation methods, i.e., recall and precision based on binary relevance assessments, to graded relevance assessments
IR evaluation methods for retrieving highly relevant documents
, 2000
"... This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in moderu large IR e ..."
Abstract
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Cited by 414 (5 self)
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This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in moderu large IR
The use of MMR, diversity-based reranking for reordering documents and producing summaries
- In SIGIR
, 1998
"... jadeQcs.cmu.edu Abstract This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved docum ..."
Abstract
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Cited by 768 (14 self)
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jadeQcs.cmu.edu Abstract This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved
Attitudes towards Risk. Experimental Measurement in Rural India
, 1978
"... Attitudes toward risk were measured in 240 households using two methods: an interview method eliciting certainty equivalents and an experimental gambling approach with real payoffs which, at their maximum, exceeded monthly incomes of unskilled laborers. The interview method is subject to interviewer ..."
Abstract
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Cited by 353 (3 self)
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Attitudes toward risk were measured in 240 households using two methods: an interview method eliciting certainty equivalents and an experimental gambling approach with real payoffs which, at their maximum, exceeded monthly incomes of unskilled laborers. The interview method is subject
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
, 1998
"... The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categoric ..."
Abstract
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Cited by 264 (3 self)
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to categorical domains and domains with mixed numeric and categorical values. The k-modes algorithm uses a simple matching dissimilarity measure to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to update modes in the clustering process to minimise
Probabilistic Models for Information Retrieval based on Divergence from Randomness
- ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 2002
"... We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a ra ..."
Abstract
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Cited by 243 (5 self)
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We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a
Generic text summarization using relevance measure and latent semantic analysis
- in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
, 2001
"... In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The rst method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to ide ..."
Abstract
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Cited by 184 (2 self)
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In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The rst method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique
Estimation of educational borrowing constraints using returns to schooling
- Journal of Political Economy
, 2004
"... This paper measures the importance of borrowing constraints on ed-ucation decisions. Empirical identification of borrowing constraints is secured by the economic prediction that opportunity costs and direct costs of schooling affect borrowing-constrained and unconstrained persons differently. Direct ..."
Abstract
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Cited by 169 (3 self)
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This paper measures the importance of borrowing constraints on ed-ucation decisions. Empirical identification of borrowing constraints is secured by the economic prediction that opportunity costs and direct costs of schooling affect borrowing-constrained and unconstrained persons differently
Toward Scalable Learning with Non-uniform Distributions: Effects and a Multi-classifier Approach
- In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining
, 1999
"... Many factors influence the performance of a learned classifier. In this paper we study different methods of measuring performance based on a unified set of cost models and the effects of training class distribution with respect to these models. Observations from these effects help us devise a distri ..."
Abstract
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Cited by 161 (6 self)
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Many factors influence the performance of a learned classifier. In this paper we study different methods of measuring performance based on a unified set of cost models and the effects of training class distribution with respect to these models. Observations from these effects help us devise a
Importance sampling for portfolio credit risk
- Management Science
"... Monte Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measure-ment of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated obligo ..."
Abstract
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Cited by 70 (7 self)
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Monte Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measure-ment of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated
Results 1 - 10
of
2,486
