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222,622
Semantics of ContextFree Languages
 In Mathematical Systems Theory
, 1968
"... "Meaning " may be assigned to a string in a contextfree language by defining "attributes " of the symbols in a derivation tree for that string. The attributes can be defined by functions associated with each production in the grammar. This paper examines the implications of th ..."
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Cited by 568 (0 self)
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"Meaning " may be assigned to a string in a contextfree language by defining "attributes " of the symbols in a derivation tree for that string. The attributes can be defined by functions associated with each production in the grammar. This paper examines the implications
An Efficient ContextFree Parsing Algorithm
, 1970
"... A parsing algorithm which seems to be the most efficient general contextfree algorithm known is described. It is similar to both Knuth's LR(k) algorithm and the familiar topdown algorithm. It has a time bound proportional to n 3 (where n is the length of the string being parsed) in general; i ..."
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Cited by 798 (0 self)
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; it has an n 2 bound for unambiguous grammars; and it runs in linear time on a large class of grammars, which seems to include most practical contextfree programming language grammars. In an empirical comparison it appears to be superior to the topdown and bottomup algorithms studied by Griffiths
Learning Common Grammar from Multilingual Corpus
"... We propose a corpusbased probabilistic framework to extract hidden common syntax across languages from nonparallel multilingual corpora in an unsupervised fashion. For this purpose, we assume a generative model for multilingual corpora, where each sentence is generated from a language dependent pr ..."
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Cited by 2 (1 self)
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probabilistic contextfree grammar (PCFG), and these PCFGs are generated from a prior grammar that is common across languages. We also develop a variational method for efficient inference. Experiments on a nonparallel multilingual corpus of eleven languages demonstrate the feasibility of the proposed method. 1
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
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
, 2001
"... We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions ..."
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Cited by 3481 (85 self)
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We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions
Incorporating nonlocal information into information extraction systems by Gibbs sampling
 IN ACL
, 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
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Cited by 725 (25 self)
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Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling
A Program for Aligning Sentences in Bilingual Corpora
, 1993
"... This paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. The program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend ..."
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Cited by 529 (5 self)
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, showing that error rates will depend on the corpus considered; however, both were small enough to hope that the method will be useful for many language pairs. To further research on bilingual corpora, a much larger sample of Canadian Hansards (approximately 90 million words, half in English and and half
A comparison of event models for Naive Bayes text classification
, 1998
"... Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey ..."
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Cited by 1026 (26 self)
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Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e
Probabilistic ContextFree Grammars for Phonology
, 2002
"... We present a phonological probabilistic contextfree grammar, which describes the word and syllable structure of German words. The grammar is trained on a large corpus by a simple supervised method, and evaluated on a syllabification task achieving 96.88% word accuracy on word tokens, and 90.3 ..."
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Cited by 8 (0 self)
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We present a phonological probabilistic contextfree grammar, which describes the word and syllable structure of German words. The grammar is trained on a large corpus by a simple supervised method, and evaluated on a syllabification task achieving 96.88% word accuracy on word tokens, and 90
Conditional probabilistic contextfree grammars
, 2004
"... In this note we present a discriminative framework for learning distributions over parse trees of contextfree languages, which we call conditional probabilistic contextfree grammars (CPCFGs). The bestperforming approaches to learning statistical parsing models are generative, in that they estimat ..."
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Cited by 8 (2 self)
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In this note we present a discriminative framework for learning distributions over parse trees of contextfree languages, which we call conditional probabilistic contextfree grammars (CPCFGs). The bestperforming approaches to learning statistical parsing models are generative
Results 1  10
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222,622