Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Role of verbs in document analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Role of word sense disambiguation in lexical acquisition: predicting semantics from syntactic cues
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Using a probabilistic class-based lexicon for lexical ambiguity resolution
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Inducing German semantic verb classes from purely syntactic subcategorisation information
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Semi-supervised verb class discovery using noisy features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Experiments on the Automatic Induction of German Semantic Verb Classes
Computational Linguistics
A general feature space for automatic verb classification
Natural Language Engineering
SemEval'07 task 19: frame semantic structure extraction
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Automatic fine-grained semantic classification for domain adaptation
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Supervised learning of a probabilistic lexicon of verb semantic classes
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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The choice of verb features is crucial for the learning of verb classes. This paper presents clustering experiments on 168 German verbs, which explore the relevance of features on three levels of verb description, purely syntactic frame types, prepositional phrase information and selectional preferences. In contrast to previous approaches concentrating on the sparse data problem, we present evidence for a linguistically defined limit on the usefulness of features which is driven by the idiosyncratic properties of the verbs and the specific attributes of the desired verb classification.