Wide-coverage efficient statistical parsing with CCG and log-linear models

Wide-coverage efficient statistical parsing with CCG and log-linear models (2007)

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by Stephen Clark , James R. Curran

Venue:	COMPUTATIONAL LINGUISTICS
Citations:	87 - 20 self

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Datum	Value	Source
TITLE	Wide-coverage efficient statistical parsing with CCG and log-linear models	user correction
AUTHOR NAME	Stephen Clark	user correction
AUTHOR AFFIL	Oxford University	user correction
AUTHOR NAME	James R. Curran	user correction
AUTHOR AFFIL	University of Sydney	user correction
ABSTRACT	This paper describes a number of log-linear parsing models for an automatically extracted lexicalized grammar. The models are "full" parsing models in the sense that probabilities are defined for complete parses, rather than for independent events derived by decomposing the parse tree. Discriminative training is used to estimate the models, which requires incorrect parses for each sentence in the training data as well as the correct parse. The lexicalized grammar formalism used is Combinatory Categorial Grammar (CCG), and the grammar is automatically extracted from CCGbank, a CCG version of the Penn Treebank. The combination of discriminative training and an automatically extracted grammar leads to a significant memory requirement (over 20 GB), which is satisfied using a parallel implementation of the BFGS optimisation algorithm running on a Beowulf cluster. Dynamic programming over a packed chart, in combination with the parallel implementation, allows us to solve one of the largest-scale estimation problems in the statistical parsing literature in under three hours. A key component of the parsing system, for both training and testing, is a Maximum Entropy supertagger which assigns CCG lexical categories to words in a sentence. The supertagger makes the discriminative training feasible, and also leads to a highly efficient parser. Surprisingly,	user correction
YEAR	2007	INFERENCE
VENUE	COMPUTATIONAL LINGUISTICS	user correction
VENUE TYPE	JOURNAL	INFERENCE
VOLUME	33	INFERENCE
CITATIONS	101 found	ParsCit 1.0