Automatic labeling of semantic roles
Computational Linguistics
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
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The Journal of Machine Learning Research
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Computational Linguistics - Special issue on using large corpora: II
Using corpus statistics and WordNet relations for sense identification
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ANLC '88 Proceedings of the second conference on Applied natural language processing
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Corpus-based statistical sense resolution
HLT '93 Proceedings of the workshop on Human Language Technology
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
Single-classifier memory-based phrase chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
A lightweight semantic chunking model based on tagging
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Evaluation of utility of LSA for word sense discrimination
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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This paper discusses an algorithm for identifying semantic arguments of a verb, word senses of a polysemous word, noun phrases in a sentence. The heart of the algorithm is a probabilistic graphical model. In contrast with other existed graphical models, such as Naive Bayes models, CRFs, HMMs, and MEMMs, this model determines a sequence of optimal class assignments among M choices for a sequence of N input symbols without using dynamic programming, running fast---O(MN), and taking less memory space---O(M). Experiments conducted on standard data sets show encourage results.