Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Lexical disambiguation using simulated annealing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
A maximum entropy approach to HowNet-based Chinese word sense disambiguation
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SenseLearner: word sense disambiguation for all words in unrestricted text
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Currently most word sense disambiguation (WSD) systems are relatively individual word sense experts. Scarcely do these systems take word sense transitions between senses of linearly consecutive words or syntactically dependent words into consideration. Word sense transitions are very important. They embody the fluency of semantic expression and avoid sparse data problem effectively. In this paper, How Net knowledge base is used to decompose every word sense into several sememes. Then one transition between two words' senses becomes multiple transitions between sememes. Sememe transitions are much easier to be captured than word sense transitions due to much less sememes. When sememes are labeled, WSD is done. In this paper, multi-layered conditional random fields (MLCRF) is proposed to model sememe transitions. The experiments show that MLCRF performs better than a base-line system and a maximum entropy model. Syntactic and hypernym features can enhance the performance significantly.