Efficient Part-of-Speech Tagging with a Min-Max Modular Neural-Network Model
Applied Intelligence
Tagging English text with a probabilistic model
Computational Linguistics
Detecting errors within a corpus using anomaly detection
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Hybrid neuro and rule-based part of speech taggers
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
IEEE Transactions on Neural Networks
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This paper proposes an on-line error detecting method for a manually annotated corpus using min-max modular (M3) neural networks. The basic idea of the method is to use guaranteed convergence of the M3 network to detect errors in learning data. To confirm the effectiveness of the method, a preliminary computer experiment was performed on a small Japanese corpus containing 217 sentences. The results show that the method can not only detect errors within a corpus, but may also discover some kinds of knowledge or rules useful for natural language processing.