Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Target word detection and semantic role chunking using support vector machines
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
An empirical study of Chinese chunking
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Labeling chinese predicates with semantic roles
Computational Linguistics
Towards robust semantic role labeling
Computational Linguistics
Improving Chinese semantic role classification with hierarchical feature selection strategy
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Automatic semantic role labeling for Chinese verbs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantic role chunking combining complementary syntactic views
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantics-driven shallow parsing for Chinese semantic role labeling
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Improving Chinese semantic role labeling with rich syntactic features
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Joint inference for bilingual semantic role labeling
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A minimum error weighting combination strategy for Chinese semantic role labeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Word-based and character-based word segmentation models: comparison and combination
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Improving chinese event construction extraction with lexical relation pairs
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
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Most existing systems for Chinese Semantic Role Labeling (SRL) make use of full syntactic parses. In this paper, we evaluate SRL methods that take partial parses as inputs. We first extend the study on Chinese shallow parsing presented in (Chen et al., 2006) by raising a set of additional features. On the basis of our shallow parser, we implement SRL systems which cast SRL as the classification of syntactic chunks with IOB2 representation for semantic roles (i.e. semantic chunks). Two labeling strategies are presented: 1) directly tagging semantic chunks in one-stage, and 2) identifying argument boundaries as a chunking task and labeling their semantic types as a classification task. Lor both methods, we present encouraging results, achieving significant improvements over the best reported SRL performance in the literature. Additionally, we put forward a rule-based algorithm to automatically acquire Chinese verb formation, which is empirically shown to enhance SRL.