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Active Estimation of FMeasures
"... We address the problem of estimating the Fαmeasure of a given model as accurately as possible on a fixed labeling budget. This problem occurs whenever an estimate cannot be obtained from heldout training data; for instance, when data that have been used to train the model are held back for reasons ..."
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Cited by 5 (1 self)
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We address the problem of estimating the Fαmeasure of a given model as accurately as possible on a fixed labeling budget. This problem occurs whenever an estimate cannot be obtained from heldout training data; for instance, when data that have been used to train the model are held back
Decoding by Linear Programming
, 2004
"... This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector f ∈ Rn from corrupted measurements y = Af + e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible to rec ..."
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Cited by 1399 (16 self)
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This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector f ∈ Rn from corrupted measurements y = Af + e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible
Discriminative Reranking for Natural Language Parsing
, 2005
"... This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this i ..."
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Cited by 333 (9 self)
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model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75 % Fmeasure, a 13 % relative decrease in Fmeasure error over the baseline model’s score of 88.2%. The article also introduces a
An Exact Algorithm for FMeasure Maximization
"... The Fmeasure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multilabel classification, and structured output prediction. Optimizing this measure remains a statistically and computationally challengin ..."
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The Fmeasure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multilabel classification, and structured output prediction. Optimizing this measure remains a statistically and computationally
Optimizing Fmeasure with support vector machines
 In Proceedings of the international
, 2003
"... Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high Fmeasure, although this particular application of SVMs has not been rig ..."
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Cited by 19 (0 self)
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Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high Fmeasure, although this particular application of SVMs has not been
Strictly Proper Scoring Rules, Prediction, and Estimation
, 2007
"... Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he ..."
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Cited by 373 (28 self)
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Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F
Named Entity Recognition through Classifier Combination
 IN PROCEEDINGS OF CONLL2003
, 2003
"... This paper presents a classifiercombination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformationbased learning, and hidden Markov model) are combined under different conditions. When no gazetteer or o ..."
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Cited by 116 (5 self)
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or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the Fmeasure error by a factor of 15
Bayesian Compressive Sensing
, 2007
"... The data of interest are assumed to be represented as Ndimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M ≪ N of basisfunction coefficients associated with B. Compressive sensing ..."
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Cited by 330 (24 self)
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the underlying signal f, “error bars ” are also estimated, these giving a measure of confidence in the inverted signal; (ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient
Automatic Committed Belief Tagging
"... We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an Fmeasure of 64%, a relative reduction in Fmeasure error of 2 ..."
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Cited by 14 (3 self)
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We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an Fmeasure of 64%, a relative reduction in Fmeasure error
Improving the Quality of Minority Class Identification in Dialog Act Tagging
"... We present a method of improving the performance of dialog act tagging in identifying minority classes by using perclass feature optimization and a method of choosing the class based not on confidence, but on a cascade of classifiers. We show that it gives a minority class Fmeasure error reduction ..."
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Cited by 1 (1 self)
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We present a method of improving the performance of dialog act tagging in identifying minority classes by using perclass feature optimization and a method of choosing the class based not on confidence, but on a cascade of classifiers. We show that it gives a minority class Fmeasure error
Results 1  10
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877,782