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A Systematic Comparison of Various Statistical Alignment Models
 Computational Linguistics
, 2003
"... this article the problem of finding the word alignment of a bilingual sentencealigned corpus by using languageindependent statistical methods. There is a vast literature on this topic, and many different systems have been suggested to solve this problem. Our work follows and extends the methods in ..."
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Cited by 1250 (58 self)
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this article the problem of finding the word alignment of a bilingual sentencealigned corpus by using languageindependent statistical methods. There is a vast literature on this topic, and many different systems have been suggested to solve this problem. Our work follows and extends the methods introduced by Brown, Della Pietra, Della Pietra, and Mercer (1993) by using refined statistical models for the translation process. The basic idea of this approach is to develop a model of the translation process with the word alignment as a hidden variable of this process, to apply statistical estimation theory to compute the "optimal" model parameters, and to perform alignment search to compute the best word alignment
Two decades of statistical language modeling: Where do we go from here
 Proceedings of the IEEE
, 2000
"... Statistical Language Models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them here ..."
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Cited by 147 (1 self)
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Statistical Language Models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them here, point to a few promising directions, and argue for a Bayesian approach to integration of linguistic theories with data. 1. OUTLINE Statistical language modeling (SLM) is the attempt to capture regularities of natural language for the purpose of improving the performance of various natural language applications. By and large, statistical language modeling amounts to estimating the probability distribution of various linguistic units, such as words, sentences, and whole documents. Statistical language modeling is crucial for a large variety of language technology applications. These include speech recognition (where SLM got its start), machine translation, document classification and routing, optical character recognition, information retrieval, handwriting recognition, spelling correction, and many more. In machine translation, for example, purely statistical approaches have been introduced in [1]. But even researchers using rulebased approaches have found it beneficial to introduce some elements of SLM and statistical estimation [2]. In information retrieval, a language modeling approach was recently proposed by [3], and a statistical/information theoretical approach was developed by [4]. SLM employs statistical estimation techniques using language training data, that is, text. Because of the categorical nature of language, and the large vocabularies people naturally use, statistical techniques must estimate a large number of parameters, and consequently depend critically on the availability of large amounts of training data.
Similaritybased models of word cooccurrence probabilities
 Machine Learning
, 1999
"... Abstract. In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach ” and “eat a beach ” is more likely. Statistical NLP met ..."
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Cited by 90 (0 self)
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Abstract. In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach ” and “eat a beach ” is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on “most similar ” words. We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudoword disambiguation. In the language modeling task, a similaritybased model is used to improve probability estimates for unseen bigrams in a backoff language model. The similaritybased method yields a 20 % perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speechrecognition error. We also compare four similaritybased estimation methods against backoff and maximumlikelihood estimation methods on a pseudoword sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easytodisambiguate highfrequency configurations. The similaritybased methods perform up to 40 % better on this particular task.
A Bit of Progress in Language Modeling
, 2001
"... Language modeling is the art of determining the probability of a sequence of words. This is useful in a large variety of areas including speech recognition, optical character recognition, handwriting recognition, machine translation, and spelling correction (Church, 1988; Brown et al., 1990; Hull, 1 ..."
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Cited by 87 (2 self)
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Language modeling is the art of determining the probability of a sequence of words. This is useful in a large variety of areas including speech recognition, optical character recognition, handwriting recognition, machine translation, and spelling correction (Church, 1988; Brown et al., 1990; Hull, 1992; Kernighan et al., 1990; Srihari and Baltus, 1992). The most commonly used language models are very simple (e.g. a Katzsmoothed trigram model). There are many improvements over this simple model however, including caching, clustering, higherorder ngrams, skipping models, and sentencemixture models, all of which we will describe below. Unfortunately, these more complicated techniques have rarely been examined in combination. It is entirely possible that two techniques that work well separately will not work well together, and, as we will show, even possible that some techniques will work better together than either one does by itself. In this...
An Efficient Method for Determining Bilingual Word Classes
"... In statistical natural language processing we always face the problem of sparse data. One way to reduce this problem is to group words into equivalence classes which is a standard method in statistical language modeling. In this paper we describe a method to determine bilingual word classes s ..."
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Cited by 61 (8 self)
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In statistical natural language processing we always face the problem of sparse data. One way to reduce this problem is to group words into equivalence classes which is a standard method in statistical language modeling. In this paper we describe a method to determine bilingual word classes suitable for statistical ma chine translation. We develop an opti mization criterion based on a maximum likelihood approach and describe a clustering algorithm. We will show that the usage of the bilingual word classes we get can improve statistical machine transla tion.
Algorithms For Bigram And Trigram Word Clustering
 Speech Communication
, 1995
"... . This paper presents and analyzes improved algorithms for clustering bigram and trigram word equivalence classes, and their respective results: 1) We give a detailed time complexity analysis of bigram clustering algorithms. 2) We present an improved implementation of bigram clustering so that large ..."
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Cited by 55 (0 self)
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. This paper presents and analyzes improved algorithms for clustering bigram and trigram word equivalence classes, and their respective results: 1) We give a detailed time complexity analysis of bigram clustering algorithms. 2) We present an improved implementation of bigram clustering so that large corpora (38 million words and more) can be clustered within a small number of days or even hours. 3) We extend the clustering approach from bigrams to trigrams. 4) We present experimental results on a 38 million word training corpus. 1. INTRODUCTION Word equivalence classes are a method for improving undertrained word Mgram language models [1], [2], [4]. Words are grouped into classes, and each word belongs to only one such class. Thus, if a word pair is not seen in training, it is quite likely that the corresponding class pair is seen. For bigram and trigram class models, we have the equations p(wn jwn\Gamma1 ) = p0(wn jG(wn)) (1) \Deltap 1(G(wn)jG(wn\Gamma1)) p(wn jwn\Gamma2 ; wn\Gam...
A bit of progress in language modeling — extended version
, 2001
"... 1.1 Overview Language modeling is the art of determining the probability of a sequence of words. This is useful in a large variety of areas including speech recognition, ..."
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Cited by 43 (1 self)
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1.1 Overview Language modeling is the art of determining the probability of a sequence of words. This is useful in a large variety of areas including speech recognition,
Similaritybased approaches to natural language processing
, 1997
"... Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and naturallanguagebased user interfaces. However, even huge ..."
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Cited by 40 (3 self)
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Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and naturallanguagebased user interfaces. However, even huge bodies of text yield highly unreliable estimates of the probability of relatively common events, and, in fact, perfectly reasonable events may not occur in the training data at all. This is known as the sparse data problem. Traditional approaches to the sparse data problem use crude approximations. We propose a different solution: if we are able to organize the data into classes of similar events, then, if information about an event is lacking, we can estimate its behavior from information about similar events. This thesis presents two such similaritybased approaches, where, in general, we measure similarity by the KullbackLeibler divergence, an informationtheoretic quantity. Our first approach is to build soft, hierarchical clusters: soft, because each event belongs to each cluster with some probability; hierarchical, because cluster centroids are iteratively split to model finer distinctions. Our clustering method, which uses the technique of deterministic annealing,
On the Effectiveness of the Skew Divergence for Statistical Language Analysis
 In Artificial Intelligence and Statistics 2001
, 2001
"... Estimating word cooccurrence probabilities is a problem underlying many applications in statistical natural language processing. ..."
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Cited by 34 (0 self)
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Estimating word cooccurrence probabilities is a problem underlying many applications in statistical natural language processing.
Hierarchical probabilistic neural network language model
 In AISTATS
, 2005
"... In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbo ..."
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Cited by 33 (2 self)
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In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbols) in a continuous space that helps to smooth the language model and provide good generalization even when the number of training examples is insufficient. However, these models are extremely slow in comparison to the more commonly used ngram models, both for training and recognition. As an alternative to an importance sampling method proposed to speedup training, we introduce a hierarchical decomposition of the conditional probabilities that yields a speedup of about 200 both during training and recognition. The hierarchical decomposition is a binary hierarchical clustering constrained by the prior knowledge extracted from the WordNet semantic hierarchy. 1