review · Journal Of Big Data
Natural language processing tools rely heavily on part of speech tagging, a foundational task that assigns grammatical tags to words based on their sentence context. Despite widespread development, part of speech taggers continue to struggle with tagging unknown words, resolving contextual ambiguity, and improving accuracy while lowering false-positive rates. Machine learning and deep learning techniques have emerged as prominent methods to tackle these persistent issues. A systematic examination of recent developments categorises the primary machine learning and deep learning approaches used to construct taggers, evaluating their relative strengths, limitations, and performance evaluation metrics. By analysing these techniques, key research gaps are identified alongside recommendations to guide the future development of more robust, efficient, and accurate tagging models.
Accurate natural language processing depends on correctly identifying the grammatical roles of words in varying contexts. Understanding how modern machine learning and deep learning models perform helps practitioners identify unresolved technical hurdles, such as handling ambiguous phrases and novel vocabulary. Addressing these gaps is crucial for refining text analysis tools that power broader information and communications technologies.
The abstract reviews foundational machine learning and deep learning methods rather than a specific commercial product, placing the work at the stage of early-stage research and synthesis. Developers of natural language processing software and text analysis systems can use the identified strengths, limitations, and metrics to guide tool selection. However, the abstract does not indicate a direct commercial application pathway or specific deployment readiness level.
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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.
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DOI: 10.1186/s40537-022-00561-y
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