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
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
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We present a probabilistic graphical model for identifying noun phrase patterns in texts. This model is derived from mathematical processes under two reasonable conditional independence assumptions with different perspectives compared with other graphical models, such as CRFs or MEMMs. Empirical results shown our model is effective. Experiments on WSJ data from the Penn Treebank, our method achieves an average of precision 97.7% and an average of recall 98.7%. Further experiments on the CoNLL-2000 shared task data set show our method achieves the best performance compared to competing methods that other researchers have published on this data set. Our average precision is 95.15% and an average recall is 96.05%.