Paper: A New Perceptron Algorithm for Sequence Labeling with Non-Local Features

ACL ID D07-1033
Title A New Perceptron Algorithm for Sequence Labeling with Non-Local Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We cannot use non-local features with cur- rent major methods of sequence labeling such as CRFs due to concerns about com- plexity. We propose a new perceptron algo- rithm that can use non-local features. Our algorithm allows the use of all types of non-local features whose values are deter- minedfromthesequenceandthelabels. The weights of local and non-local features are learned together in the training process with guaranteed convergence. We present experi- mental results from the CoNLL 2003 named entity recognition (NER) task to demon- strate the performance of the proposed algo- rithm.