A corpus-based approach to language learning
A corpus-based approach to language learning
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Segmenting unrestricted Chinese text into prosodic words instead of lexical words
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
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Hierarchical prosody structure generation is a key component for a speech synthesis system. One major feature of the prosody of Mandarin Chinese speech flow is prosodic phrase grouping. In this paper we proposed an approach for prediction of Chinese prosodic phrase boundaries in unrestricted Chinese text, which combines Conditional Random Fields (CRFs) model and TBL model. First a CRFs model is trained to predict the prosodic phrase boundaries. After that we apply a TBL based error driven learning approach to amend the initial prediction. A comparison is conducted between the new model and HMM for prosodic phrase break prediction. Experiments show that the combined approach improves overall performance. The precision and recall ratio are improved.