A Comparative Study of Manual and Automated POS Tagging: Insights into Accuracy, Scalability, and Application Contexts

Abstract

Part-of-Speech (POS) tagging, essential in Natural Language Processing (NLP), involves assigning grammatical categories to words in a text. This paper presents a comparative study of manual and automated POS tagging approaches, focusing on their accuracy, scalability, and application contexts. Manual POS tagging, performed by expert annotators, ensures high precision by leveraging human judgment to handle linguistic nuances and complex structures. In contrast, automated POS tagging, exemplified by the CLAWS tagger, offers significant efficiency and scalability, processing large volumes of text quickly using rule-based algorithms and pre-tagged corpora. Through a detailed examination of a Grade 10 English textbook, this study evaluates the performance of both methods. Results indicate that while manual tagging excels in accuracy, particularly with complex and ambiguous texts, automated methods like CLAWS are more scalable but may struggle with nuanced linguistic features. The findings highlight the trade-offs between the high precision of manual tagging and the processing speed of automated systems, emphasizing the need to choose the appropriate tagging method based on specific application requirements. Future research could explore hybrid approaches to leverage the strengths of both manual and automated tagging methods for enhanced performance across various NLP tasks.