Results 1  10
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240
Regular models of phonological rule systems." Paper presented to
 Oxford University
, 1988
"... This paper presents a set of mathematical and computational tools for manipulating and reasoning about regular languages and regular relations and argues that they provide a solid basis for computational phonology. It shows in detail how this framework applies to ordered sets of contextsensitive re ..."
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Cited by 335 (5 self)
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This paper presents a set of mathematical and computational tools for manipulating and reasoning about regular languages and regular relations and argues that they provide a solid basis for computational phonology. It shows in detail how this framework applies to ordered sets of contextsensitive rewriting rules and also to grammars in Koskenniemi's twolevel formalism. This analysis provides a common representation of phonological constraints that supports efficient generation and recognition by a single simple interpreter. 1.
Towards Historybased Grammars: Using Richer Models for Probabilistic Parsing
 In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics
, 1993
"... We describe a generarive probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel ..."
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Cited by 158 (6 self)
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We describe a generarive probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. In headtohead tests against one of the best existing robust probabilistic parsing models, which we call PCFG, the HBG model significantly outperforms PCFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.
Speech Recognition by Composition of Weighted Finite Automata
 FINITESTATE LANGUAGE PROCESSING
, 1996
"... We present a general framework based on weighted finite automata and weighted finitestate transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including contextdependent u ..."
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Cited by 124 (12 self)
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We present a general framework based on weighted finite automata and weighted finitestate transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including contextdependent units, pronunciation dictionaries, language models and lattices. Furthermore, general but efficient algorithms can used for combining information sources in actual recognizers and for optimizing their application. In particular, a single composition algorithm is used both to combine in advance information sources such as language models and dictionaries, and to combine acoustic observations and information sources dynamically during recognition.
Efficient Parsing for Bilexical ContextFree Grammars and Head Automaton Grammars
 IN ACL 37
, 1999
"... Several recent stochastic parsers use bilexical grammars, where each word type idiosyncratically prefers particular complements with particular head words. We present O(n^4) parsing algorithms for two bilexical formalisms, improving the prior upper bounds of O(n^5). For a common special case that wa ..."
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Cited by 92 (19 self)
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Several recent stochastic parsers use bilexical grammars, where each word type idiosyncratically prefers particular complements with particular head words. We present O(n^4) parsing algorithms for two bilexical formalisms, improving the prior upper bounds of O(n^5). For a common special case that was known to allow O(n³) parsing (Eisner, 1997), we present an O(n³) algorithm with an improved grammar constant.
Tree Insertion Grammar: A CubicTime, Parsable Formalism that Lexicalizes ContextFree Grammar without Changing the Trees Produced
 Computational Linguistics
, 1994
"... this paper, we study the problem of lexicalizing contextfree grammars and show that it enables faster processing. In previous attempts to take advantage of lexicalization, a variety of lexicalization procedures have been developed that convert contextfree grammars (CFGs) into equivalent lexicalize ..."
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Cited by 77 (1 self)
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this paper, we study the problem of lexicalizing contextfree grammars and show that it enables faster processing. In previous attempts to take advantage of lexicalization, a variety of lexicalization procedures have been developed that convert contextfree grammars (CFGs) into equivalent lexicalized grammars. However, these procedures typically suffer from one or more of the following problems
ContextFree Languages and PushDown Automata
 Handbook of Formal Languages
, 1997
"... Contents 1. Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.1 Grammars : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 Examples : : : : : : : : : : : : : : : : : : : : : : : : : : : ..."
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Cited by 62 (0 self)
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Contents 1. Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.1 Grammars : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 Examples : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2. Systems of equations : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.1 Systems : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.2 Resolution : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 11 2.3 Linear systems : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 12 2.4 Parikh's theorem : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :
Conjunctive Grammars
"... This paper introduces a class of formal grammars made up by augmenting the formalism of contextfree grammars with an explicit settheoretic intersection operation. ..."
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Cited by 59 (33 self)
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This paper introduces a class of formal grammars made up by augmenting the formalism of contextfree grammars with an explicit settheoretic intersection operation.
Weighted rational transductions and their application to human language processing
 In ARPA Workshop on Human Language Technology
, 1994
"... We present the concepts of weighted language, ~ansduction and automaton from algebraic automata theory as a general framework for describing and implementing decoding cascades in speech and language processing. This generality allows us to represent uniformly such information sources as pronunciat ..."
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Cited by 50 (8 self)
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We present the concepts of weighted language, ~ansduction and automaton from algebraic automata theory as a general framework for describing and implementing decoding cascades in speech and language processing. This generality allows us to represent uniformly such information sources as pronunciation dictionaries, language models artd lattices, and to use uniform algorithms for building decoding stages and for optimizing and combining them. In particular, a single automata join algorithm can be used either to combine information sources such as a pronunciation dictionary and a contextdependency model during the construction of a decoder, or dynamically during the operation of the decoder. Applications to speech recognition and to Chinese text segmentation will be discussed. 1.
Natural language grammatical inference with recurrent neural networks
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 1998
"... This paper examines the inductive inference of a complex grammar with neural networks  specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the P ..."
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Cited by 45 (1 self)
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This paper examines the inductive inference of a complex grammar with neural networks  specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or GovernmentandBinding theory. Neural networks are trained, without the division into learned vs. innate components assumed by Chomsky, in an attempt to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. How a recurrent neural network could possess linguistic capability and the properties of various common recurrent neural network architectures are discussed. The problem exhibits training behavior which is often not present with smaller grammars and training was initially difficult. However, after implementing several techniques aimed at improving the convergence of the gradient descent backpropagationthroughtime training algorithm, significant learning was possible. It was found that certain architectures are better able to learn an appropriate grammar. The operation of the networks and their training is analyzed. Finally, the extraction of rules in the form of deterministic finite state automata is investigated.