Automatic labeling of semantic roles
Computational Linguistics
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Support Vector Learning for Semantic Argument Classification
Machine Learning
Annotating the propositions in the Penn Chinese Treebank
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Question answering based on semantic structures
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Semantic role labeling: an introduction to the special issue
Computational Linguistics
Labeling chinese predicates with semantic roles
Computational Linguistics
Semantic argument classification exploiting argument interdependence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automatic semantic role labeling for Chinese verbs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Hierarchical semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Chinese semantic role labeling with shallow parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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
A minimum error weighting combination strategy for Chinese semantic role labeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Combining constituent and dependency syntactic views for Chinese semantic role labeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
A Corpus-Based Method to Improve Feature-Based Semantic Role Labeling
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Integrative semantic dependency parsing via efficient large-scale feature selection
Journal of Artificial Intelligence Research
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In recent years, with the development of Chinese semantically annotated corpus, such as Chinese Proposition Bank and Normalization Bank, the Chinese semantic role labeling (SRL) task has been boosted. Similar to English, the Chinese SRL can be divided into two tasks: semantic role identification (SRI) and classification (SRC). Many features were introduced into these tasks and promising results were achieved. In this paper, we mainly focus on the second task: SRC. After exploiting the linguistic discrepancy between numbered arguments and ARGMs, we built a semantic role classifier based on a hierarchical feature selection strategy. Different from the previous SRC systems, we divided SRC into three sub tasks in sequence and trained models for each sub task. Under the hierarchical architecture, each argument should first be determined whether it is a numbered argument or an ARGM, and then be classified into fine-gained categories. Finally, we integrated the idea of exploiting argument interdependence into our system and further improved the performance. With the novel method, the classification precision of our system is 94.68%, which outperforms the strong baseline significantly. It is also the state-of-the-art on Chinese SRC.