The Role of Lexicalization and Pruning for Base Noun Phrase Grammars

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Natural Language and Information Retrieval

This paper explores the role of lexicalization and pruning of grammars for base noun phrase identification. We modify the original framework of Cardie & Pierce (1998) to extract lexicalized treebank grammars that assign a score to each potential noun phrase based upon both the part-of-speech tag sequence and the word sequence of the phrase. We evaluate the modified framework on the "simple" and "complex" base NP corpora of the original study. As expected, we find that lexicalization dramatically improves the performance of the unpruned treebank grammars; however, for the simple base noun phrase data set, the lexicalized grammar performs below the unlexicalized, but pruned, grammar of the original base NP study, suggesting that lexicalization is not critical for recognizing very simple, relatively unambiguous constituents. Somewhat surprisingly, we also find that error-driven pruning improves the performance of the probabilistic, lexicalized base noun phrase grammars by up to 1.0% recall and 0.4% precision, and does so even using the original pruning strategy that fails to distinguish the effects of lexicalization. This result may have implications for many probabilistic grammar-based approaches to problems in natural language processing: error-driven pruning is a remarkably robust method for improving the performance of probabilistic and non-probabilistic grammars alike.