Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
Automatic labeling of semantic roles
Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
Tree kernels for semantic role labeling
Computational Linguistics
Generalized Framework for Syntax-Based Relation Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Engineering of syntactic features for shallow semantic parsing
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Towards open-domain Semantic Role Labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Semantic role labeling for open information extraction
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
An analysis of open information extraction based on semantic role labeling
Proceedings of the sixth international conference on Knowledge capture
A domain-independent statistical methodology for dialog management in spoken dialog systems
Computer Speech and Language
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Most Spoken Dialog Systems are based on speech grammars and frame/slot semantics. The semantic descriptions of input utterances are usually defined ad-hoc with no ability to generalize beyond the target application domain or to learn from annotated corpora. The approach we propose in this paper exploits machine learning of frame semantics, borrowing its theoretical model from computational linguistics. While traditional automatic Semantic Role Labeling approaches on written texts may not perform as well on spoken dialogs, we show successful experiments on such porting. Hence, we design and evaluate automatic FrameNet-based parsers both for English written texts and for Italian dialog utterances. The results show that disfluencies of dialog data do not severely hurt performance. Also, a small set of FrameNet-like manual annotations is enough for realizing accurate Semantic Role Labeling on the target domains of typical Dialog Systems.