## Abstract A parts of speech (POS) tagging system using neural networks has been developed by Ma and colleagues. This system can tag unlearned data with a much higher accuracy than that of the Hidden Markov Model (HMM), which is the most popular method of POS tagging. It does so by learning a smal
β¦ LIBER β¦
Efficient Part-of-Speech Tagging with a Min-Max Modular Neural-Network Model
β Scribed by Bao-Liang Lu; Qing Ma; Michinori Ichikawa; Hitoshi Isahara
- Book ID
- 110441914
- Publisher
- Springer US
- Year
- 2003
- Tongue
- English
- Weight
- 506 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0924-669X
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Our recent development of a computational cochlear-nucleus-like network model for the study of speech encoding mechanisms associated with parts of the auditory system central to the auditory nerve is described in this paper. This network model is based on physiological grounds, and is designed to gr