On-Line Error Detection of Annotated Corpus Using Modular Neural Networks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Typical sample selection and redundancy reduction for min-max modular network with GZC function
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
An algorithm for pruning redundant modules in min-max modular network with GZC function
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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This paper presents a part-of-speech tagging method based on a min-max modular neural-network model. The method has three main steps. First, a large-scale tagging problem is decomposed into a number of relatively smaller and simpler subproblems according to the class relations among a given training corpus. Secondly, all of the subproblems are learned by smaller network modules in parallel. Finally, following two simple module combination laws, all of the trained network modules are integrated into a modular parallel tagging system that produces solutions to the original tagging problem. The proposed method has several advantages over existing tagging systems based on multilayer perceptrons. (1) Training times can be drastically reduced and desired learning accuracy can be easily achieved; (2) the method can scale up to larger tagging problems; (3) the tagging system has quick response and facilitates hardware implementation. In order to demonstrate the effectiveness of the proposed method, we perform simulations on two different language corpora: a Thai corpus and a Chinese corpus, which have 29,028 and 45,595 ambiguous words, respectively. We also compare our method with several existing tagging models including hidden Markov models, multilayer perceptrons and neuro-taggers. The results show that both the learning accuracy and generalization performance of the proposed tagging model are better than statistical models and multilayer perceptrons, and they are comparable to the most successful tagging models.