Paper: Guided Learning for Bidirectional Sequence Classification

ACL ID P07-1096
Title Guided Learning for Bidirectional Sequence Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

In this paper, we propose guided learning, a new learning framework for bidirectional sequence classification. The tasks of learn- ing the order of inference and training the local classifier are dynamically incorporated into a single Perceptron like learning algo- rithm. We apply this novel learning algo- rithm to POS tagging. It obtains an error rate of 2.67% on the standard PTB test set, which represents 3.3% relative error reduction over the previous best result on the same data set, while using fewer features.