Part of speech tagging: a systematic review of deep learning and machine learning approaches

Abstract

Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. 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. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. 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. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. 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. 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.

Comprehensibility and Automation: Plain Language in the Era of Digitalization

“…Such methods are widely used in various NLP tasks like sentiment analysis (e.g., Liu, 2012;Cabanlit & Espinosa, 2014) or part-of-speech tagging (Chiche & Yitagesu;.…”

BaNeP: An End-to-End Neural Network Based Model for Bangla Parts-of-Speech Tagging

“…Apart from these methods, another popular and efficient approach for POS-Tagging tasks is using different artificial neural networkbased algorithms like Multi-layer perceptron (MLE), CNN, RNN, GRU, LSTM, BiLSTM, etc. [33]. A neural networkbased architecture was proposed to determine the POS tag in the Odia language by Das RB et al [34].…”

Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

“…In the proposed work Syntactic features are extracted from Kannada articles to understand the author's writing style. The basic concepts of POS tagging and the different methodology to implement it is discussed in [23]. Authors have served the complete POS tagging information for beginners to carry out research in this domain.…”

“…Few researchers have experimented on both Instance and Profilebased approaches for both global languages and a few Indian local Languages which include short and long texts. For the majority of the AA tasks, the deep learning technique [23] proved to be efficient but can't be claimed as a standardized technique since ML algorithms also outperformed well for other data samples.…”