Selection and information: a class-based approach to lexical relationships
Selection and information: a class-based approach to lexical relationships
Class-based probability estimation using a semantic hierarchy
Computational Linguistics
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
Using semantic preferences to identify verbal participation in role switching alternations
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Integrating compositional semantics into a verb lexicon
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Acquiring lexical generalizations from corpora: a case study for diathesis alternations
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Crosslinguistic transfer in automatic verb classification
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Inducing German semantic verb classes from purely syntactic subcategorisation information
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Gathering knowledge for a question answering system from heterogeneous information sources
HLTKM '01 Proceedings of the workshop on Human Language Technology and Knowledge Management - Volume 2001
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We propose a new method for detecting verb alternations, by comparing the probability distributions over WordNet classes occurring in two potentially alternating argument positions. Existing distance measures compute only the distributional distance, and do not take into account the semantic similarity between WordNet senses across the distributions. Our method compares two probability distributions over WordNet by measuring the semantic distance of the component nodes, weighted by their probability. To incorporate semantic similarity, we calculate the (dis)similarity between two probability distributions as a weighted distance "travelled" from one to the other through the WordNet hierarchy. We evaluate the measure on the causative alternation, and find that overall it outperforms existing distance measures.