Artificial Intelligence - Special volume on natural language processing
Computational Linguistics
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Automated induction of sense in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Polysemy in verbs: systematic relations between senses and their effect on annotation
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
SemEval-2007 task 17: English lexical sample, SRL and all words
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
GLML: annotating argument selection and coercion
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
UTDMet: Combining WordNet and corpus data for argument coercion detection
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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In this paper, we describe the Argument Selection and Coercion task, currently in development for the SemEval-2 evaluation exercise scheduled for 2010. This task involves characterizing the type of compositional operation that exists between a predicate and the arguments it selects. Specifically, the goal is to identify whether the type that a verb selects is satisfied directly by the argument, or whether the argument must change type to satisfy the verb typing. We discuss the problem in detail and describe the data preparation for the task.