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86
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 272 (9 self)
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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 initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the loglikelihood under a baseline 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 new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods within loglinear (maximumentropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.
Convexity, Classification, and Risk Bounds
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2003
"... Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 01 loss function. The convexity makes these algorithms computationally efficien ..."
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Cited by 119 (12 self)
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Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 01 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. To study these issues, we provide a general quantitative relationship between the risk as assessed using the 01 loss and the risk as assessed using any nonnegative surrogate loss function. We show that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function: that it satisfy a pointwise form of Fisher consistency for classification. The relationship is based on a simple variational transformation of the loss function that is easy to compute in many applications. We also present a refined version of this result in the case of low noise. Finally, we
An introduction to boosting and leveraging
 Advanced Lectures on Machine Learning, LNCS
, 2003
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Exponentiated gradient algorithms for conditional random fields and maxmargin Markov networks
, 2008
"... Loglinear and maximummargin models are two commonlyused methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large dat ..."
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Cited by 59 (1 self)
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Loglinear and maximummargin models are two commonlyused methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the loglinear or maxmargin objective function; the dual in both the loglinear and maxmargin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the maxmargin case, O ( 1 ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for loglinear models only O(log (1/ε)) updates are required. For both the maxmargin and loglinear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be
Parameter Estimation for Statistical Parsing Models: Theory and Practice of DistributionFree Methods
, 2001
"... A fundamental problem in statistical parsing is the choice of criteria and algorithms used to estimate the parameters in a model. The predominant approach in computational linguistics has been to use a parametric model with some variant of maximumlikelihood estimation. The assumptions under which m ..."
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Cited by 53 (10 self)
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A fundamental problem in statistical parsing is the choice of criteria and algorithms used to estimate the parameters in a model. The predominant approach in computational linguistics has been to use a parametric model with some variant of maximumlikelihood estimation. The assumptions under which maximumlikelihood estimation is justified are arguably quite strong. This paper discusses the statistical theory underlying various parameterestimation methods, and gives algorithms which depend on alternatives to (smoothed) maximumlikelihood estimation. We first give an overview of results from statistical learning theory. We then show how important concepts from the classification literature  specifically, generalization results based on margins on training data  can be derived for parsing models. Finally, we describe parameter estimation algorithms which are motivated by these generalization bounds.
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
 In Proceedings of the 19th International Conference on Machine Learning
, 2002
"... A new approach to ensemble learning is introduced that takes ranking rather than classification as fundamental, leading to models on the symmetric group and its cosets. The approach uses a generalization of the Mallows model on permutations to combine multiple input rankings. Applications incl ..."
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Cited by 45 (3 self)
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A new approach to ensemble learning is introduced that takes ranking rather than classification as fundamental, leading to models on the symmetric group and its cosets. The approach uses a generalization of the Mallows model on permutations to combine multiple input rankings. Applications include the task of combining the output of multiple search engines and multiclass or multilabel classification, where a set of input classifiers is viewed as generating a ranking of class labels.
Efficient largescale distributed training of conditional maximum entropy models
 In Advances in Neural Information Processing Systems
, 2009
"... Training conditional maximum entropy models on massive data sets requires significant computational resources. We examine three common distributed training methods for conditional maxent: a distributed gradient computation method, a majority vote method, and a mixture weight method. We analyze and c ..."
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Cited by 31 (2 self)
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Training conditional maximum entropy models on massive data sets requires significant computational resources. We examine three common distributed training methods for conditional maxent: a distributed gradient computation method, a majority vote method, and a mixture weight method. We analyze and compare the CPU and network time complexity of each of these methods and present a theoretical analysis of conditional maxent models, including a study of the convergence of the mixture weight method, the most resourceefficient technique. We also report the results of largescale experiments comparing these three methods which demonstrate the benefits of the mixture weight method: this method consumes less resources, while achieving a performance comparable to that of standard approaches. 1
Modified logistic regression: An approximation to SVM and its applications in largescale text categorization
 In Proceedings of the 20th International Conference on Machine Learning (pp. 888–895). Menlo Park, AAAI
, 2003
"... Logistic Regression (LR) has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to Support Vector Machines (SVM) and AdaBoost. In this paper, we use a modified version of LR to approximate the optimization ..."
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Cited by 29 (1 self)
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Logistic Regression (LR) has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to Support Vector Machines (SVM) and AdaBoost. In this paper, we use a modified version of LR to approximate the optimization of SVM by a sequence of unconstrained optimization problems. We prove that our approximation will converge to SVM, and propose an iterative algorithm called “MLRCG ” which uses Conjugate Gradient as its inner loop. Multiclass version “MMLRCG ” is also obtained after simple modifications. We compare the MLRCG with SVMlight over different text categorization collections, and show that our algorithm is much more efficient than SVMlight when the number of training examples is very large. Results of the multiclass version MMLRCG is also reported. 1.
Maximum entropy distribution estimation with generalized regularization
 Proc. Annual Conf. Computational Learning Theory
, 2006
"... Abstract. We present a unified and complete account of maximum entropy distribution estimation subject to constraints represented by convex potential functions or, alternatively, by convex regularization. We provide fully general performance guarantees and an algorithm with a complete convergence pr ..."
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Cited by 26 (1 self)
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Abstract. We present a unified and complete account of maximum entropy distribution estimation subject to constraints represented by convex potential functions or, alternatively, by convex regularization. We provide fully general performance guarantees and an algorithm with a complete convergence proof. As special cases, we can easily derive performance guarantees for many known regularization types, including ℓ1, ℓ2, ℓ 2 2 and ℓ1 + ℓ 2 2 style regularization. Furthermore, our general approach enables us to use information about the structure of the feature space or about sample selection bias to derive entirely new regularization functions with superior guarantees. We propose an algorithm solving a large and general subclass of generalized maxent problems, including all discussed in the paper, and prove its convergence. Our approach generalizes techniques based on information geometry and Bregman divergences as well as those based more directly on compactness. 1
Boosted classification trees and class probability/quantile estimation
 Journal of Machine Learning Research
, 2006
"... The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class probability function P[y = 1x]. Boosted classification trees are known to perform qu ..."
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Cited by 24 (4 self)
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The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class probability function P[y = 1x]. Boosted classification trees are known to perform quite well for such problems. In this article we consider the use of standard, offtheshelf boosting for two more general problems: 1) classification with unequal costs or, equivalently, classification at quantiles other than 1/2, and 2) estimation of the conditional class probability function P[y = 1x]. We first examine whether the latter problem, estimation of P[y = 1x], can be solved with LogitBoost, and with AdaBoost when combined with a natural link function. The answer is negative: both approaches are often ineffective because they overfit P[y = 1x] even though they perform well as classifiers. A major negative point of the present article is the disconnect between class probability estimation and classification. Next we consider the practice of over/undersampling of the two classes. We present an algorithm that uses AdaBoost in conjunction with Over/UnderSampling and Jittering of the data (“JOUSBoost”). This algorithm is simple, yet successful, and it preserves the advantage of relative protection against overfitting, but for arbitrary misclassification costs and, equivalently, arbitrary quantile boundaries. We then use collections of classifiers obtained from a grid of quantiles to form estimators of class probabilities. The estimates of the class probabilities compare favorably to those obtained by a variety of methods across both simulated and real data sets.