PCFG Learning by Nonterminal Partition Search
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning grammars for different parsing tasks by partition search
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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We develop a language model using probabilistic context-free grammars (PCFGs) that is ``pseudo context-sensitive'''' in that the probability that a non-terminal $N$ expands using a rule $r$ depends on $N$''s parent. We derive the equations for estimating the necessary probabilities using a variant of the inside-outside algorithm. We give experimental results showing that, beginning with a high-performance PCFG, one can develop a pseudo PCSG that yields significant performance gains. Analysis shows that the benefits from the context-sensitive statistics are localized, suggesting that we can use them to extend the original PCFG. Experimental results confirm that this is both feasible and the resulting grammar retains the performance gains. This implies that our scheme may be useful as a novel method for PCFG induction.