Grammar Acquisition Based on Clustering Analysis and Its Application to Statistical Parsing

This paper proposes a new method for learning a context-sensitive conditional probability context-free grammar from an unlabeled bracketed corpus based on clustering analysis and describes a natural language parsing model which uses a probability-based scoring function of the grammar to rank parses of a sentence. By grouping brackets in a corpus into a number of similar bracket groups based on their local contextual information, the corpus is automatically labeled with some nonterminal labels, and consequently a grammar with conditional probabilities is acquired. The statistical parsing model provides a framework for nding the most likely parse of a sentence based on these conditional probabilities. Experiments using Wall Street Journal data show that our approach achieves a relatively high accuracy: 88 % recall, 72 % precision and 0.7 crossing brackets per sentence for sentences shorter than 10 words, and 71 % recall, 51 % precision and 3.4 crossing brackets for sentences between 10-19 words. This result supports the assumption that local contextual statistics obtained from an unlabeled bracketed corpus are e ective for learning a useful grammar and parsing.