Results 1  10
of
189
Hierarchical phrasebased translation
 Computational Linguistics
, 2007
"... We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous contextfree grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from b ..."
Abstract

Cited by 588 (9 self)
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We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous contextfree grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from both syntaxbased translation and phrasebased translation. We describe our system’s training and decoding methods in detail, and evaluate it for translation speed and translation accuracy. Using BLEU as a metric of translation accuracy, we find that our system performs significantly better than the Alignment Template System, a stateoftheart phrasebased system. 1.
Better kbest parsing
, 2005
"... We discuss the relevance of kbest parsing to recent applications in natural language processing, and develop efficient algorithms for kbest trees in the framework of hypergraph parsing. To demonstrate the efficiency, scalability and accuracy of these algorithms, we present experiments on Bikel’s i ..."
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Cited by 192 (16 self)
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We discuss the relevance of kbest parsing to recent applications in natural language processing, and develop efficient algorithms for kbest trees in the framework of hypergraph parsing. To demonstrate the efficiency, scalability and accuracy of these algorithms, we present experiments on Bikel’s implementation of Collins ’ lexicalized PCFG model, and on Chiang’s CFGbased decoder for hierarchical phrasebased translation. We show in particular how the improved output of our algorithms has the potential to improve results from parse reranking systems and other applications. 1
Algorithms for Deterministic Incremental Dependency Parsing
 Computational Linguistics
, 2008
"... Parsing algorithms that process the input from left to right and construct a single derivation have often been considered inadequate for natural language parsing because of the massive ambiguity typically found in natural language grammars. Nevertheless, it has been shown that such algorithms, combi ..."
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Cited by 114 (20 self)
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Parsing algorithms that process the input from left to right and construct a single derivation have often been considered inadequate for natural language parsing because of the massive ambiguity typically found in natural language grammars. Nevertheless, it has been shown that such algorithms, combined with treebankinduced classifiers, can be used to build highly accurate disambiguating parsers, in particular for dependencybased syntactic representations. In this article, we first present a general framework for describing and analyzing algorithms for deterministic incremental dependency parsing, formalized as transition systems. We then describe and analyze two families of such algorithms: stackbased and listbased algorithms. In the former family, which is restricted to projective dependency structures, we describe an arceager and an arcstandard variant; in the latter family, we present a projective and a nonprojective variant. For each of the four algorithms, we give proofs of correctness and complexity. In addition, we perform an experimental evaluation of all algorithms in combination with SVM classifiers for predicting the next parsing action, using data from thirteen languages. We show that all four algorithms give competitive accuracy, although the nonprojective listbased algorithm generally outperforms the projective algorithms for languages with a nonnegligible proportion of nonprojective constructions. However, the projective algorithms often produce comparable results when combined with the technique known as pseudoprojective parsing. The linear time complexity of the stackbased algorithms gives them an advantage with respect to efficiency both in learning and in parsing, but the projective listbased algorithm turns out to be equally efficient in practice. Moreover, when the projective algorithms are used to implement pseudoprojective parsing, they sometimes become less efficient in parsing (but not in learning) than the nonprojective listbased algorithm. Although most of the algorithms have been partially described in the literature before, this is the first comprehensive analysis and evaluation of the algorithms within a unified framework. 1.
Parsing InsideOut
, 1998
"... Probabilistic ContextFree Grammars (PCFGs) and variations on them have recently become some of the most common formalisms for parsing. It is common with PCFGs to compute the inside and outside probabilities. When these probabilities are multiplied together and normalized, they produce the probabili ..."
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Cited by 99 (2 self)
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Probabilistic ContextFree Grammars (PCFGs) and variations on them have recently become some of the most common formalisms for parsing. It is common with PCFGs to compute the inside and outside probabilities. When these probabilities are multiplied together and normalized, they produce the probability that any given nonterminal covers any piece of the input sentence. The traditional use of these probabilities is to improve the probabilities of grammar rules. In this thesis we show that these values are useful for solving many other problems in Statistical Natural Language Processing. We give a framework for describing parsers. The framework generalizes the inside and outside values to semirings. It makes it easy to describe parsers that compute a wide variety of interesting quantities, including the inside and outside probabilities, as well as related quantities such as Viterbi probabilities and nbest lists. We also present three novel uses for the inside and outside probabilities. T...
Semiring Parsing
 Computational Linguistics
, 1999
"... this paper is that all five of these commonly computed quantities can be described as elements of complete semirings (Kuich 1997). The relationship between grammars and semirings was discovered by Chomsky and Schtitzenberger (1963), and for parsing with the CKY algorithm, dates back to Teitelbaum ( ..."
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Cited by 86 (1 self)
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this paper is that all five of these commonly computed quantities can be described as elements of complete semirings (Kuich 1997). The relationship between grammars and semirings was discovered by Chomsky and Schtitzenberger (1963), and for parsing with the CKY algorithm, dates back to Teitelbaum (1973). A complete semiring is a set of values over which a multiplicative operator and a commutative additive operator have been defined, and for which infinite summations are defined. For parsing algorithms satisfying certain conditions, the multiplicative and additive operations of any complete semiring can be used in place of/x and , and correct values will be returned. We will give a simple normal form for describing parsers, then precisely define complete semirings, and the conditions for correctness
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 83 (2 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
Statistical Machine Translation by Parsing
, 2004
"... In an ordinary syntactic parser, the input is a string, and the grammar ranges over strings. This paper explores generalizations of ordinary parsing algorithms that allow the input to consist of string tuples and/or the grammar to range over string tuples. Such algorithms can infer the synchronous s ..."
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Cited by 77 (6 self)
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In an ordinary syntactic parser, the input is a string, and the grammar ranges over strings. This paper explores generalizations of ordinary parsing algorithms that allow the input to consist of string tuples and/or the grammar to range over string tuples. Such algorithms can infer the synchronous structures hidden in parallel texts. It turns out that these generalized parsers can do most of the work required to train and apply a syntaxaware statistical machine translation system.
Parsing and hypergraphs
 In IWPT
, 2001
"... While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension o ..."
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Cited by 77 (3 self)
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While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an Ç Ò time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as allowed by the inherent ordering of probabilistic dependencies. 1
Forest rescoring: Faster decoding with integrated language models
 In ACL ’07
, 2007
"... Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on kbest parsing algorithms and demonstrate their effectiveness on bo ..."
Abstract

Cited by 71 (0 self)
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Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on kbest parsing algorithms and demonstrate their effectiveness on both phrasebased and syntaxbased MT systems. In both cases, our methods achieve significant speed improvements, often by more than a factor of ten, over the conventional beamsearch method at the same levels of search error and translation accuracy. 1