Results 1  10
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16,257
Three Generative, Lexicalised Models for Statistical Parsing
, 1997
"... In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised contextfree gram mar. We then extend the model to in clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show that the parse ..."
Abstract

Cited by 570 (8 self)
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In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised contextfree gram mar. We then extend the model to in clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show
A New Statistical Parser Based on Bigram Lexical Dependencies
, 1996
"... This paper describes a new statistical parser which is based on probabilities of dependencies between headwords in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal ..."
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Cited by 490 (4 self)
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This paper describes a new statistical parser which is based on probabilities of dependencies between headwords in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street
Statistical phrasebased translation
, 2003
"... We propose a new phrasebased translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrasebased translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrasebased models outpe ..."
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Cited by 944 (11 self)
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We propose a new phrasebased translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrasebased translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrasebased models
Statistical DecisionTree Models for Parsing
 In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics
, 1995
"... Syntactic natural language parsers have shown themselves to be inadequate for processing highlyambiguous largevocabulary text, as is evidenced by their poor per formance on domains like the Wall Street Journal, and by the movement away from parsingbased approaches to textprocessing in gen ..."
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Cited by 367 (1 self)
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in general. In this paper, I describe SPATTER, a statistical parser based on decisiontree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too
Statistical Parsing with a Contextfree Grammar and Word Statistics
, 1997
"... We describe a parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence. This model is used in a parsing system by finding the parse for the sentence with the highest probability. This system outperforms previou ..."
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Cited by 414 (18 self)
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We describe a parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence. This model is used in a parsing system by finding the parse for the sentence with the highest probability. This system outperforms
Semantic similarity based on corpus statistics and lexical taxonomy
 Proc of 10th International Conference on Research in Computational Linguistics, ROCLING’97
, 1997
"... This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantifie ..."
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Cited by 873 (0 self)
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This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better
Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
 J. Geophys. Res
, 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
Abstract

Cited by 800 (23 self)
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. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter
Active Appearance Models.
 IEEE Transactions on Pattern Analysis and Machine Intelligence,
, 2001
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and graylevel variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations ..."
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Cited by 2154 (59 self)
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AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and graylevel variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations
The Dantzig selector: statistical estimation when p is much larger than n
, 2005
"... In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n ≪ ..."
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Cited by 879 (14 self)
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In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n
Results 1  10
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16,257