Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Semi-supervised training for the averaged perceptron POS tagger
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
An architecture for complex clinical question answering
Proceedings of the 1st ACM International Health Informatics Symposium
Semisupervised condensed nearest neighbor for part-of-speech tagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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This paper presents a novel way of improving POS tagging on heterogeneous data. First, two separate models are trained (generalized and domain-specific) from the same data set by controlling lexical items with different document frequencies. During decoding, one of the models is selected dynamically given the cosine similarity between each sentence and the training data. This dynamic model selection approach, coupled with a one-pass, left-to-right POS tagging algorithm, is evaluated on corpora from seven different genres. Even with this simple tagging algorithm, our system shows comparable results against other state-of-the-art systems, and gives higher accuracies when evaluated on a mixture of the data. Furthermore, our system is able to tag about 32K tokens per second. We believe that this model selection approach can be applied to more sophisticated tagging algorithms and improve their robustness even further.