![]() ![]() So, Part of Speech (POS) Tagging is a notable NLP topic that aims in assigning each word of a text the proper syntactic tag in its context of appearance. Part-of-speech (POS) tagging is one of the most important addressed areas and main building block and application in the natural language processing discipline. Thus, it improves human-to-human communication, enables human-to-machine communication by doing useful processing of texts or speeches. It is also defined as a computerized approach to process and understand natural language. In particular, NLP is an automatic approach to analyzing texts using a different set of technologies and theories with the help of a computer. ![]() It aids people in many areas, such as information retrieval, information extraction, machine translation, question-answering speech synthesis and recognition, and so on. Natural language processing (NLP) has become a part of daily life and a crucial tool today. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. This article first clarifies the concept of part of speech POS tagging. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. As a result, many different NLP tools are being produced. Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. ![]()
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