A Quick Intensive Course on Natural Language Processing Applied to Literary Studies

This paper presents how Natural Language Processing is taught to students of a Master’s Degree in Literary Studies. These students’ background is solely humanistic, and they have no knowledge whatsoever of Natural Language Processing (NLP). The challenge is to introduce these students to the main aspects of NLP in a 20-hour course, and show them how they can apply these techniques to the analysis of literary texts. The course focuses on three main aspects: first, to get to know a new approach to literary text analysis based on the distant reading model; second, to develop a representative literary corpus and, finally, to apply basic NLP techniques to said corpus in order to extract relevant data. Among these techniques are word frequencies, Part of Speech tagging and distributional semantic models such as LDA Topic Modeling. Satisfaction surveys show that students are satisfied with the course.