Paper: Linguistically-Motivated Grammar Extraction Generalization and Adaptation

ACL ID I05-1016
Title Linguistically-Motivated Grammar Extraction Generalization and Adaptation
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

In order to obtain a high precision and high coverage grammar, we proposed a model to measure grammar coverage and designed a PCFG parser to measure efficiency of the grammar. To generalize grammars, a grammar binari- zation method was proposed to increase the coverage of a probabilistic context- free grammar. In the mean time linguistically-motivated feature constraints were added into grammar rules to maintain precision of the grammar. The gen- eralized grammar increases grammar coverage from 93% to 99% and bracket- ing F-score from 87% to 91% in parsing Chinese sentences. To cope with error propagations due to word segmentation and part-of-speech tagging errors, we also proposed a grammar blending method to adapt to such errors. The blended grammar can reduce about 20~30% of pars...