Results 1  10
of
100
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 190 (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
Adaptor grammars: a framework for specifying compositional nonparametric Bayesian models
 In Advances in Neural Information Processing Systems 19
, 2007
"... This paper introduces adaptor grammars, a class of probabilistic models of language that generalize probabilistic contextfree grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with “adaptors ” that can induce dependencies among successive uses. With a particular choice o ..."
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Cited by 117 (19 self)
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This paper introduces adaptor grammars, a class of probabilistic models of language that generalize probabilistic contextfree grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with “adaptors ” that can induce dependencies among successive uses. With a particular choice of adaptor, based on the PitmanYor process, nonparametric Bayesian models of language using Dirichlet processes and hierarchical Dirichlet processes can be written as simple grammars. We present a generalpurpose inference algorithm for adaptor grammars, making it easy to define and use such models, and illustrate how several existing nonparametric Bayesian models can be expressed within this framework. 1
Computational Complexity of Probabilistic Disambiguation by means of TreeGrammars
, 1996
"... This paper studies the compntational complexity of dlsambiguation under probabilistic treegrammars as in (Bod, 1992; Schabes and Waters, 1993). It presents a proof that the following problems are NPhard: computing the Most Probable Parse frmn a sentence or from a wordgraph, and computing t ..."
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Cited by 114 (7 self)
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This paper studies the compntational complexity of dlsambiguation under probabilistic treegrammars as in (Bod, 1992; Schabes and Waters, 1993). It presents a proof that the following problems are NPhard: computing the Most Probable Parse frmn a sentence or from a wordgraph, and computing the Most Pro'oable Sentence (MPS) from a word graph. The NPhardness of computing the MPS from a wordgraph also holds for Stochastic ContextFree Gram mars (SCFGs).
Parsing with Compositional Vector Grammars
"... Natural language parsing has typically been done with small sets of discrete categories such as NP and VP, but this representation does not capture the full syntactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases or splitting categories only par ..."
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Cited by 107 (5 self)
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Natural language parsing has typically been done with small sets of discrete categories such as NP and VP, but this representation does not capture the full syntactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases or splitting categories only partly address the problem at the cost of huge feature spaces and sparseness. Instead, we introduce a Compositional Vector Grammar (CVG), which combines PCFGs with a syntactically untied recursive neural network that learns syntacticosemantic, compositional vector representations. The CVG improves the PCFG of the Stanford Parser by 3.8 % to obtain an F1 score of 90.4%. It is fast to train and implemented approximately as an efficient reranker it is about 20 % faster than the current Stanford factored parser. The CVG learns a soft notion of head words and improves performance on the types of ambiguities that require semantic information such as PP attachments. 1
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 85 (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
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
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 76 (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 biomedical literature
 In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP05), Jeju Island, Korea
, 2005
"... Abstract. We present a preliminary study of several parser adaptation techniques evaluated on the GENIA corpus of MEDLINE abstracts [1, 2]. We begin by observing that the Penn Treebank (PTB) is lexically impoverished when measured on various genres of scientific and technical writing, and that this ..."
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Cited by 71 (2 self)
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Abstract. We present a preliminary study of several parser adaptation techniques evaluated on the GENIA corpus of MEDLINE abstracts [1, 2]. We begin by observing that the Penn Treebank (PTB) is lexically impoverished when measured on various genres of scientific and technical writing, and that this significantly impacts parse accuracy. To resolve this without requiring indomain treebank data, we show how existing domainspecific lexical resources may be leveraged to augment PTBtraining: partofspeech tags, dictionary collocations, and namedentities. Using a stateoftheart statistical parser [3] as our baseline, our lexicallyadapted parser achieves a 14.2 % reduction in error. With oracleknowledge of namedentities, this error reduction improves to 21.2%. 1
Bayesian inference for PCFGs via Markov chain Monte Carlo
 In Proceedings of the North American Conference on Computational Linguistics (NAACL ’07
, 2007
"... This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximumlikelihood estimation using the InsideOutside algorithm. We illustrate these methods by estimating a sp ..."
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Cited by 70 (9 self)
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This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximumlikelihood estimation using the InsideOutside algorithm. We illustrate these methods by estimating a sparse grammar describing the morphology of the Bantu language Sesotho, demonstrating that with suitable priors Bayesian techniques can infer linguistic structure in situations where maximum likelihood methods such as the InsideOutside algorithm only produce a trivial grammar. 1