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Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture

Publication Type : Conference Paper

Thematic Areas : Scientific reports

Authors : Dr. E. A. Gopalakrishnan, Dr. Soman K. P., B. Premjith

Source : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India (2019)

Url : https://ieeexplore.ieee.org/abstract/document/8944559

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science

Year : 2019

Abstract : POS tagging is the process of classifying words into their parts of speech like noun, verb, preposition etc. to a word. It is the most important and basic process in NLP. It is acts as an essential preprocess for other applications in natural language processing (NLP) like sentiment analysis, NER, speech recognition and so on. POS tagging is treated as a sequence labeling problem in which it labels words with their appropriate Part-Of-Speech. This work implementing a POS tagger for biomedical domain using deep neural network architecture. The experiment is RNN, LSTM, and GRU will give better performance since they are able to access more context information and which we evaluated using publicly accessible dataset from GENIA. Most of the applications in NLP became solved due to the advancement of neural network or deep learning.

Cite this Research Publication : A. Gopalakrishnan, Dr. Soman K. P., and B. Premjith, “Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture”, in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019.

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