Context-sensitive statistics for improved grammatical language models
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Artificial Intelligence - Special volume on empirical methods
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
PCFG models of linguistic tree representations
Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Chinese treebanks and grammar extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
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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 binarization 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 generalized grammar increases grammar coverage from 93% to 99% and bracketing 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 parsing errors due to error assignment of pos made by a word segmentation system.