Part of speech tagging with min-max modular neural networks

Part of speech tagging with min-max modular neural networks

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 small Thai corpus on the order of 10,000 words that are ambiguous as to their POSs. However, the threelayer percep...

Massively Parallel Part of Speech Tagging Using Min-Max Modular Neural Networks

This paper presents a massively parallel tagging method for automatically assigning the correct part of speech (POS) tag to each ambiguous word in a sentence in the context of the sentence. This method is based on the min-max modular neural network, an e cient modular neural network model for solving large-scale pattern recognition problems. The method has two attractive features. One is that i...

Part-Of-Speech Tagging With Neural Networks

Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method hased on neural networks (Net-Tagger) is presented and its performance is compared to that of a llMM-tagger (Cutting et al., 1992) and a trigrambased tagger (Kempe, 1993). It is shown that the Net-Tagger performs as well as the trigram...

Part-of-Speech Tagging with Recurrent Neural Networks

Part-of-Speech Tagging with Recurrent Neural Networks

This paper explores the use of discrete-time recurrent neural networks for part-of-speech disambiguation of textual corpora. Our approach does not need a handtagged text for training the tagger, being probably the first neural approach doing so. Preliminary results show that the performance of this approach is, at least, similar to that of a standard hidden Markov model trained using the Baum-W...