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20
A Maximum Entropy Approach to Adaptive Statistical Language Modeling
 Computer, Speech and Language
, 1996
"... An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's histor ..."
Abstract

Cited by 241 (11 self)
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An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution...
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.
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...
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,
Adaptive language modeling using minimum discriminant estimation
 Association for Computational Linguistics
, 1992
"... We present an algorithm to adapt a ngram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that already occurred. Then, we find the closest ngram distribution to the static n.gram distribution (using the discrimin ..."
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Cited by 41 (2 self)
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We present an algorithm to adapt a ngram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that already occurred. Then, we find the closest ngram distribution to the static n.gram distribution (using the discrimination information distance measure) and that satisfies the marginal constraints derived from the document. The resulting minimum discrimination information model results in a perplexity of 208 instead of 290 for the static trigram model on a document of 321 words. 1
A Hybrid Approach To Adaptive Statistical Language Modeling
 Proceedings of the ARPA workshop on human language technology
, 1994
"... We desert'be our latest attempt at adaptive language modeling. At the heart of our approach is a Maximum Entropy (ME) model which inc.orlxnates many knowledge sources in a consistent manner. The other components are a selective unigram cache, a conditional bigram cache, and a conventionalstatic trig ..."
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Cited by 23 (2 self)
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We desert'be our latest attempt at adaptive language modeling. At the heart of our approach is a Maximum Entropy (ME) model which inc.orlxnates many knowledge sources in a consistent manner. The other components are a selective unigram cache, a conditional bigram cache, and a conventionalstatic trigram. We describe the knowledge sources used to build such a model with ARPA's official WSJ corpus, and report on perplexity and word error rate results obtained with it. Then, three different adaptation paradigms are discussed, and an additional experiment, based on AP wire data, is used to compare them. 1. OVERVIEW OF ME FRAMEWORK Using several different probability estimates to arrive at one combined estimate is a general problem that arises in many tasks. The Maximum Entropy (ME) principle has recently been demonstrated as a powerful tool for combining statistical estimates from diverse sources[l, 2, 3]. The ME principle ([4, 5]) proposes the following: 1. Reformulate the different estimates as constraints on the expectation of various functions, to be satisfied by the target (combined) estimate. 2. Among all probability distributions that satisfy these constraints, choose the one that has the highest entropy. More specifically, for estimating a probability function P(x), each constraint i is associated with a constraintfunctionfi(x) and a desired expectation ci. The constraint is then written as: def E Eefi = P(x)fi(x) = ci. (1) X Given consistent constraints, a unique ME solutions is guaranteed to exist, and to be of the form: P(x) = II mf'°°, (2) i where the pi's are some unknown constants, to be found. Probability functions of the form (2) are called loglinear, and the family of functions defined by holding thefi's fixed and varying the pi's is called an exponential family.
Incorporation of a markov model of language syntax in a text recognition algorithm
 In Sympostum on Doc ...ment Analysis and Information Retrievals
"... The use of a hidden Markov model (HMM) for language syntax to improve the performance of a text recognition algorithm is proposed. Syntactic constraints are described by the transition probabilities between word classes. The confusion between the feature string for a word and the various syntactic c ..."
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Cited by 21 (6 self)
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The use of a hidden Markov model (HMM) for language syntax to improve the performance of a text recognition algorithm is proposed. Syntactic constraints are described by the transition probabilities between word classes. The confusion between the feature string for a word and the various syntactic classes is also described probabilistically. A modification of the Viterbi algorithm is also proposed that finds a fixed number of sequences of syntactic classes for a given sentence that have the highest probabilities of occurrence, given the feature strings for the words. An experimental application of this approach is demonstrated with a word hypothesization algorithm that produces a number of guesses about the identity of each word in a running text The use of first and second order transition probabilities is explored. Overall performance of between 65 and 80 percent reduction in the average number of words that can match a given image is achieved. 1.
How to Wreck a Nice Beach You Sing Calm Incense
 Proceedings of the 10th international conference on Intelligent user interfaces
, 2005
"... A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the "best " candidate. If the choice is in ..."
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Cited by 14 (3 self)
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A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the "best " candidate. If the choice is incorrect, the user must invoke a correction interface that displays a list of the hypotheses and choose the desired one. The correction interface is timeconsuming, and accounts for much of the frustration of today's dictation systems. Conventional dictation systems prioritize hypotheses based on language models derived from statistical techniques such as ngrams and Hidden Markov Models. We propose a supplementary method for ordering hypotheses based on Commonsense Knowledge. We filter acoustical and wordfrequency hypotheses by testing their plausibility with a semantic network derived from 700,000 statements about everyday life. This often filters out possibilities that "don't make sense " from the user's viewpoint, and leads to improved recognition. Reducing the hypothesis space in this way also makes possible streamlined correction interfaces that improve the overall throughput of dictation systems.
Lattice Based Language Models
, 1997
"... This paper introduces lattice based language models, a new language modeling paradigm. These models construct multidimensional hierarchies of partitions and select the most promising partitions to generate the estimated distributions. We discussed a specific two dimensional lattice and propose two ..."
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Cited by 11 (1 self)
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This paper introduces lattice based language models, a new language modeling paradigm. These models construct multidimensional hierarchies of partitions and select the most promising partitions to generate the estimated distributions. We discussed a specific two dimensional lattice and propose two primary features to measure the usefulness of each node: the trainingset history count and the smoothed entropy of its prediction. Smoothing techniques are reviewed and a generalization of the conventional backoff strategy to multiple dimensions is proposed. Preliminary experimental results are obtained on the SWITCHBOARD corpus which lead to a 6.5 % perplexity reduction over a word trigram model. Project sponsored by the National Security Agency under Grant No. MDA9049710006. The United States Government is authorized to reproduce and distribute reprints notwithstanding any copyright notation hereon. y Current address: D'ept. Math., Universit'e Jean Monnet, 23, rue P. Michelon, 42023 S...