Automatic stochastic tagging of natural language texts
Computational Linguistics
Task adaptation in stochastic language model for Chinese homophone disambiguation
ACM Transactions on Asian Language Information Processing (TALIP)
Adaptive multilingual sentence boundary disambiguation
Computational Linguistics
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Markov random field based English part-of-speech tagging system
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
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Word category prediction is used to implement an accurate word recognition system. Traditional statistical approaches require considerable training data to estimate the probabilities of word sequences, and many parameters to memorize probabilities. To solve this problem, NETgram, which is the neural network for word category prediction, is proposed. Training results show that the performance of the NETgram is comparable to that of the statistical model although the NETgram requires fewer parameters than the statistical model. Also the NETgram performs effectively for unknown data, i.e., the NETgram interpolates sparse training data. Results of analyzing the hidden layer show that the word categories are classified into linguistically significant groups. The results of applying the NETgram to HMM English word recognition show that the NETgram improves the word recognition rate from 81.0% to 86.9%