Paper: A Boosted Semi-Markov Perceptron

ACL ID W13-3506
Title A Boosted Semi-Markov Perceptron
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013

This paper proposes a boosting algorithm that uses a semi-Markov perceptron. The training algorithm repeats the training of a semi-Markov model and the update of the weights of training samples. In the boost- ing, training samples that are incorrectly segmented or labeled have large weights. Such training samples are aggressively learned in the training of the semi-Markov perceptron because the weights are used as the learning ratios. We evaluate our training method with Noun Phrase Chunk- ing, Text Chunking and Extended Named Entity Recognition. The experimental re- sults show that our method achieves better accuracy than a semi-Markov perceptron and a semi-Markov Conditional Random Fields.