Hidden Markov Models for Part-of-Speech Tagging

Understanding the structure of human language is crucial for building intelligent systems. Part-of-speech tagging is a fundamental step in making text data comprehensible to computers. This course will equip you with a solid understanding of Hidden Markov Models (HMMs), a classic yet powerful technique, enabling you to build and analyze systems that automatically identify the grammatical role of words in sentences. What you'll learn: * Understand the foundational concepts of Hidden Markov Models, including states, transitions, and emissions. * Apply HMMs to solve the problem of Part-of-Speech (POS) tagging in natural language processing. * Learn the Viterbi algorithm for efficiently determining the most probable sequence of hidden states. * Practice evaluating the performance of POS taggers using relevant metrics. * Grasp how HMMs form a basis for more advanced sequence modeling in NLP. The course begins with essential NLP terminology and the mathematical foundations of HMMs, progressing through practical implementation steps for POS tagging. You will then explore methods for evaluating model accuracy and discuss challenges like handling unseen data. This course is designed for absolute beginners in Natural Language Processing and machine learning who want to understand a core algorithm for sequence modeling. No prior experience with HMMs or advanced mathematics is required. Start your journey into understanding how machines can interpret human language.