Title: Comparative study and experimentation on different techniques for customised POS-tagging

Authors: Chandni Magoo; Manjeet Singh

Addresses: Department of Computer Applications, J.C. Bose University of Science and Technology (YMCA), India; Department of Computer Science, Manav Rachna University, India ' Department of Computer Applications, J.C. Bose University of Science and Technology, India

Abstract: In recent years, NLP is playing a crucial role in giving solutions to the problems like text summarisation, text classification, document similarity identification, and interactive systems. POS-tagging is one basic operation on a given natural language processing text. But present POS-taggers suffer from some serious problems. In this paper, we are identifying the limitations of the present POS-taggers and propose a new set of tags incorporated with the existing best POS-taggers (along with other tags). It is presumably suitable for paraphrasing, which will augment the dataset being prepared for the supervised learning of the interactive system. The performance of these POS-taggers is also measured experimentally.

Keywords: natural language processing; NLP; Stanford POS tagging; bi-LSTM; machine learning classifiers; transfer learning; NLTK.

DOI: 10.1504/IJIIDS.2022.126515

International Journal of Intelligent Information and Database Systems, 2022 Vol.15 No.4, pp.420 - 449

Received: 25 May 2021
Accepted: 07 Nov 2021

Published online: 27 Oct 2022 *

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