A New Deep Neural Networks Based Model for Part of Speech Tagging

Document Type : Original Article

Authors

1 Ph.D Candidate, Faculty of Computer Engineering, Technical and Engineering Campus, University of Yazd, Yazd, Iran

2 Associate Professor, Faculty of Computer Engineering, Technical and Engineering Campus, University of Yazd, Yazd, Iran

Abstract

Part of speech tagging is an important issue in natural language processing and is the base of many other major subjects in this field. In this article, a new method have been introduced for part of speech tagging using deep neural networks. The purpose of this method is solving problems that common other methods are facing with which are extract deep features from texts and classifying these features. The proposed method is based on that we can find deep features and product optimal output by using small deep neural networks. This method was implemented using Tensorflow's specialized libraries and Keras API in python and was evaluated on coNLL2000 standard dataset. The experimental results show that the proposed method is capable to extract high level features from natural language's words and is able to achieve considerable accuracy for repetitive tags. In addition, this method is able to be used in variant environments and on different devices.

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Main Subjects

References

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Applied and basic Machine intelligence research
Volume 1, Issue 1
March 2023
Pages 23-33

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