Part-of-Speech (POS) tagging is the process of assigning a grammatical part of speech—such as a noun, verb, adjective, or adverb—to each word within a text. This task functions as a form of classification, where each word token is labeled with its appropriate POS tag. As a foundational step in word-level analysis, POS tagging is fundamentally important for a wide range of more complex Natural Language Processing (NLP) applications, including syntactic parsing, semantic analysis, and information extraction.

The primary purpose of this whitepaper is to analyze and compare four major methodologies for performing Part-of-Speech tagging. Each approach represents a distinct strategy, evolving from manually encoded linguistic knowledge to sophisticated, data-driven statistical models.

The four methodologies examined in this document are:

• Rule-based POS Tagging
• Stochastic POS Tagging
• Transformation-based Tagging
• Hidden Markov Model (HMM) POS Tagging

This analysis will begin with an examination of the oldest of these techniques, the rule-based approach, which relies on direct linguistic expertise.