Results 1  10
of
332
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 340 (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 157 (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 125 (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 61 (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 60 (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 49 (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.
What is the Search Space of the Regular Inference?
 In Proceedings of the Second International Colloquium on Grammatical Inference (ICGI'94
, 1994
"... This paper revisits the theory of regular inference, in particular by extending the definition of structural completeness of a positive sample and by demonstrating two basic theorems. This framework enables to state the regular inference problem as a search through a boolean lattice built from the p ..."
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Cited by 45 (5 self)
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This paper revisits the theory of regular inference, in particular by extending the definition of structural completeness of a positive sample and by demonstrating two basic theorems. This framework enables to state the regular inference problem as a search through a boolean lattice built from the positive sample. Several properties of the search space are studied and generalization criteria are discussed. In this framework, the concept of border set is introduced, that is the set of the most general solutions excluding a negative sample. Finally, the complexity of regular language identification from both a theoritical and a practical point of view is discussed. 1 Introduction Regular inference is the process of learning a regular language from a set of examples, consisting of a positive sample, i.e. a finite subset of a regular language. A negative sample, i.e. a finite set of strings not belonging to this language, may also be available. This problem has been studied as early as th...