A REVIEW ON DIFFERENT APPROACHES OF POS TAGGING IN NLP

Abstract

Natural language processing (NLP) techniques have piqued the curiosity of many as information and communication technology has advanced rapidly. As a result, several NLP tools are being developed. However, there are several obstacles to building effective and efficient NLP systems that analyze natural languages effectively. Part of speech (POS) technique for identifying a specific phrase is tagging or words in a paragraph based on the context of the sentence/words inside the paragraph. Despite tremendous research efforts, POS tagging continues to encounter hurdles in boosting accuracy while minimizing false-positive rates and identifying unfamiliar terms. Furthermore, ambiguity must be avoided when tagging terms with distinct contextual meanings inside a phrase. Deep learning (DL) and machine learning (ML)-based POS taggers have recently been deployed as promising methods for identifying words in a particular phrase throughout a paragraph. In this post, we'll define part of speech POS tagging. It then provides comprehensive classification based on the well-known ML and DL approaches used in the design and implementation of part of speech taggers. A complete assessment of the most recent POS tagging publications is offered, with the weaknesses and merits of the suggested methodologies discussed. Then, in terms of the proposed techniques used and their performance assessment criteria, current trends and developments in DL and ML-based part-of-speech-taggers are given. Using the limitations of the offered techniques, we highlighted some research gaps and presented future research recommendations for developing DL and ML-based POS tagging.