Comparative study and experimentation on different techniques for customised POS-tagging. (6th September 2022)

• Record Type: Journal Article
• Authors: Magoo, Chandni; Singh, Manjeet
• Is Part Of: International journal of intelligent information and database systems. Volume 15:Number 4(2022)
• Page Start: 420
• Page End: 449
• Publication Date: 2022-09-06
• Subjects: natural language processing -- NLP -- Stanford POS tagging -- bi-LSTM -- machine learning classifiers -- transfer learning -- NLTK; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.