Paper: A Fast Boosting-based Learner for Feature-Rich Tagging and Chunking

ACL ID W08-2103
Title A Fast Boosting-based Learner for Feature-Rich Tagging and Chunking
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008
Authors

Combination of features contributes to a significant improvement in accuracy on tasks such as part-of-speech (POS) tag- ging and text chunking, compared with us- ing atomic features. However, selecting combination of features on learning with large-scale and feature-rich training data requires long training time. We propose a fast boosting-based algorithm for learning rules represented by combination of fea- tures. Our algorithm constructs a set of rules by repeating the process to select sev- eral rules from a small proportion of can- didate rules. The candidate rules are gen- erated from a subset of all the features with a technique similar to beam search. Then we propose POS tagging and text chunk- ing based on our learning algorithm. Our tagger and chunker use candidate POS tags or chu...