Boosting Applied to Tagging and PP Attachment

Abstract

Boosting is a machine learning algorithm that is not well known in computational linguistics. We apply it to part-of-speech tagging and prepositional phrase attachment. Performance is very encouraging. We also show how to improve data quality by using boosting to identify annotation errors. 1 Introduction Boosting is a machine learning algorithm that has been applied successfully to a variety of problems, but is almost unknown in computational linguistics. We describe experiments in which we apply boosting to part-of-speech tagging and prepositional phrase attachment. Results on both PP-attachment and tagging are within sampling error of the best previous results. The current best technique for PP-attachment (backed-off density estimation) does not perform well for tagging, and the current best technique for tagging (maxent) is below state-of-the-art on PPattachment. Boosting achieves state-of-the-art performance on both tasks simultaneously. The idea of boosting is to combine many s...