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Graph-based and transition-based approaches to dependency parsing adopt very different views of the problem, each view having its own strengths and limitations. We study both approaches under the framework of beam-search. By developing a graph-based and a transition-based dependency parser, we show that a beam-search decoder is a competitive choice for both(More)
We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed improvement. Such decoding is enabled by: (1) separating full word features from partial word features so that feature templates can be(More)
Transition-based approaches have shown competitive performance on constituent and dependency parsing of Chinese. State-of-the-art accuracies have been achieved by a deterministic shift-reduce parsing model on parsing the Chinese Treebank 2 data (Wang et al., 2006). In this paper, we propose a global discriminative model based on the shift-reduce parsing(More)
We study a range of syntactic processing tasks using a general statistical framework that consists of a global linear model, trained by the generalized perceptron together with a generic beam-search decoder. We apply the framework to word segmentation, joint segmentation and POS-tagging, dependency parsing, and phrase-structure parsing. Both components of(More)
CCGs are directly compatible with binary-branching bottom-up parsing algorithms, in particular CKY and shift-reduce algorithms. While the chart-based approach has been the dominant approach for CCG, the shift-reduce method has been little explored. In this paper, we develop a shift-reduce CCG parser using a discriminative model and beam search, and compare(More)
Machine-produced text often lacks grammat-icality and fluency. This paper studies gram-maticality improvement using a syntax-based algorithm based on CCG. The goal of the search problem is to find an optimal parse tree among all that can be constructed through selection and ordering of the input words. The search problem, which is significantly harder than(More)
A fundamental problem in text generation is word ordering. Word ordering is a com-putationally difficult problem, which can be constrained to some extent for particular applications, for example by using synchronous grammars for statistical machine translation. There have been some recent attempts at the unconstrained problem of generating a sentence from a(More)