Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Risk context effects in inductive reasoning: an experimental and computational modeling study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
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Existing computational models of human inductive reasoning have been constructed based on psychological evaluations concerning the similarities or relationships between entities. However, the costs involved in collecting psychological evaluations for the sheer number of entities that exist mean that they are prohibitively impractical. In order to avoid this problem, the present article examines three types of models: a category-based neural network model, a category-based Bayesian model, and a feature-based neural network model. These models utilize the results of a statistical analysis of a Japanese corpus computing co-occurrence probabilities for word pairs, rather than using psychological evaluations. Argument strength ratings collected by a psychological experiment were found to correlate well with simulations for the category-based neural network model.