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 contextfree 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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Hsieh, Y. M., Yang, D. C., & Chen, K. J. (2005). Linguistically-motivated grammar extraction, generalization and adaptation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3651 LNAI, pp. 177–187). https://doi.org/10.1007/11562214_16
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