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
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Support Vector Learning for Semantic Argument Classification
Machine Learning
Use of deep linguistic features for the recognition and labeling of semantic arguments
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Shallow Semantic Parsing for Lexical Units in Chinese FrameNet
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A joint model for semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling using lexical statistical information
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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The Chinese FrameNet Project is creating a lexical resource for Chinese, based on the principles of Frame Semantics and supported by corpus evidence. Due to the fact that syntactic and semantic role labeling (SSRL) is very necessary for deep natural language processing, a method based on cascaded conditional random fields (CCRFs) is proposed for the SSRL task, and the CCRFs model is trained to label the predicates' semantic roles, Phrase Types and Grammatical Functions in a sentence. The key of the methods is parameter estimation and feature selection. There are three category features for the CCRFs algorithm: features based on segmentation words, features based on the Part of Speech (POS) of the relative words, and features based on the position relative to the targets. Evaluation on the datasets of the prerelease version of Chinese FrameNet shows that the method can obtain satisfying performance and can achieve 70.45% F for syntactic & semantic role labeling.