Natural Language Processing for Digital Humanities

Humanities research often involves analyzing vast amounts of text data, a task increasingly enhanced by modern computational methods. This course teaches you the foundational principles of Natural Language Processing (NLP) and Artificial Intelligence (AI) specifically tailored for philological and digital humanities tasks. You will gain the skills to move beyond manual analysis and use automated methods to extract meaning, identify trends, and handle massive linguistic corpora. What you'll learn: * Understand the core concepts of machine learning and NLP relevant to text analysis and linguistic study. * Apply standard NLP techniques like tokenization, stemming, and part-of-speech tagging to raw textual data. * Practice working with large linguistic corpora and preparing unstructured text data for automated processing. * Design and implement simple automated text analysis workflows using modern computational libraries. * Explore basic prompt engineering techniques for leveraging large language models in research tasks. * Analyze linguistic patterns, stylistic features, and thematic trends across diverse textual datasets. The course begins with foundational definitions of AI and NLP, progressing through essential data preparation steps and classical text analysis methods. Finally, you will apply these techniques to common tasks in digital humanities research. This course is designed for beginners in data science, especially students, researchers, and professionals in philology, literature, history, and the wider humanities who wish to incorporate computational methods into their work. No prior programming or data science experience is required. Start building your computational toolkit for advanced text analysis today.