Part of speech tagging with min-max modular neural networks

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...

معرفی شبکه های عصبی پیمانه ای عمیق با ساختار فضایی-زمانی دوگانه جهت بهبود بازشناسی گفتار پیوسته فارسی

In this article, growable deep modular neural networks for continuous speech recognition are introduced. These networks can be grown to implement the spatio-temporal information of the frame sequences at their input layer as well as their labels at the output layer at the same time. The trained neural network with such double spatio-temporal association structure can learn the phonetic sequence...

Part-of-Speech Tagging with Recurrent Neural Networks

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 Mixed Approaches of Neural Networks and Transformation Rules

For the purpose of constructing a practical part of speech tagger that uses as few training data as possible, an approach using neural networks, which uses di erent lengths of contexts based on longest context priority and takes into account the maximization of information amount, have been proposed so far. To further improve the tagging performance, this paper proposes an integrated approach o...