Part-of-Speech Tagging with the Viterbi Algorithm

Unlock the power of language understanding by mastering how computers identify the grammatical role of words in text. Part-of-speech tagging is a fundamental step in Natural Language Processing, essential for more complex linguistic analysis. This course will guide you through the core concepts of part-of-speech tagging, enabling you to confidently apply the Viterbi algorithm and Hidden Markov Models to analyze and structure textual data effectively. What you'll learn: Understand the fundamental concepts of Part-of-Speech (POS) tagging and its role in NLP. Learn to preprocess text data, including tokenization, as a prerequisite for linguistic analysis. Master the principles of Hidden Markov Models (HMMs) for sequence modeling in text. Apply the Viterbi algorithm to accurately determine the most probable sequence of POS tags for a given sentence. Practice calculating transition and emission probabilities to build robust tagging models. Evaluate the performance of part-of-speech taggers using essential metrics like accuracy. The course begins with foundational text processing and statistical linguistics, then progressively introduces Hidden Markov Models and the step-by-step application of the Viterbi algorithm. You will explore practical examples and learn to interpret the results of your tagging models. This course is designed for absolute beginners with no prior experience in Natural Language Processing or advanced algorithms. No specific technical prerequisites are required, just a willingness to learn how computers understand language. Start your journey into the fascinating world of linguistic analysis and build a solid foundation in NLP.