Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Memory-based learning: using similarity for smoothing
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
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Multi-prototype vector-space models of word meaning
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A flexible, corpus-driven model of regular and inverse selectional preferences
Computational Linguistics
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This paper presents a new, exemplar-based model of thematic fit. In contrast to previous models, it does not approximate thematic fit as argument plausibility or 'fit with verb selectional preferences', but directly as semantic role plausibility for a verb-argument pair, through similarity-based generalization from previously seen verb-argument pairs. This makes the model very robust for data sparsity. We argue that the model is easily extensible to a model of semantic role ambiguity resolution during online sentence comprehension. The model is evaluated on human semantic role plausibility judgments. Its predictions correlate significantly with the human judgments. It rivals two state-of-the-art models of thematic fit and exceeds their performance on previously unseen or low-frequency items.