A Boosted Semi-Markov Perceptron
نویسنده
چکیده
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 boosting, 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 Chunking, Text Chunking and Extended Named Entity Recognition. The experimental results show that our method achieves better accuracy than a semi-Markov perceptron and a semi-Markov Conditional Random Fields.
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