Because of the growth of powerful graphics processor units (GPUs), these techniques have gained widespread recognition and appeal in the field of natural language processing, notably part of speech tagging (POST), throughout the previous decade. Under the big umbrella of artificial intelligence, both ML and DL aim to learn meaningful information from the given big language resources. To accomplish the requirements of an efficient POS tagger, the researchers have explored the possibility of using Deep learning (DL) and Machine learning (ML) techniques. On the other hand, a combination of probabilistic and rule-based approaches is the transformational-based approach to automatically calculate symbolic rules from a corpus. And probabilistic approaches determine the frequent tag of a word in a given context based on probability values calculated from a tagged corpus which is tagged manually. Rule-based part of speech taggers assign a tag to a word based on manually created linguistic rules for instance, a word that follows adjectives is tagged as a noun. Several approaches have been deployed to automatic POS tagging, like transformational-based, rule-based and probabilistic approaches. , the main issue that must be addressed in part of speech tagging is that of ambiguity: words behave differently given different contexts in most languages, and thus the difficulty is to identify the correct tag of a word appearing in a particular sentence. Manually tagging part-of-speech to words is a tedious, laborious, expensive, and time-consuming task therefore, widespread interest is becoming in automating the tagging process. The genesis of POS tagging is based on the ambiguity of many words in terms of their part of speech in a context. POS tagging is an important natural language processing application used in machine translation, word sense disambiguation, question answering parsing, and so on. A POS is a grammatical classification that commonly includes verbs, adjectives, adverbs, nouns, etc. Part-of-speech (POS) tagging, also called grammatical tagging, is the automatic assignment of part-of-speech tags to words in a sentence. 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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