A maximum entropy approach to natural language processing
Computational Linguistics
Automatic labeling of semantic roles
Computational Linguistics
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Comparison of alignment templates and maximum entropy models for natural language understanding
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
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
Maximum entropy models for FrameNet classification
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Multidimensional text analysis for eRulemaking
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Semantic argument classification exploiting argument interdependence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantic role lableing system using maximum entropy classifier
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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As part of its description of lexico-semantic predicate frames or conceptual structures, the FrameNet project defines a set of semantic roles specific to the core predicate of a sentence. Recently, researchers have tried to automatically produce semantic interpretations of sentences using this information. Building on prior work, we describe a new method to perform such interpretations. We define sentence segmentation first and show how Maximum Entropy re-ranking helps achieve a level of 76.2% F-score (answer among topfive candidates) or 61.5% (correct answer).