Graphs, Computation, and Language

Employing the properties of linguistic networks allows discovering structure and making predictions. This course seeks answers to three questions: (1) how to express the linguistic phenomena as graphs, (2) how to gain knowledge based on them, and (3) how to assess the quality of this knowledge. We will start with traditional graph-based Natural Language Processing (NLP) methods like TextRank and Markov Clustering and finish with such contemporary Machine Learning techniques as DeepWalk and Graph Convolutional Networks. As the growing interest in NLP methods urges their meaningful evaluation, we pay special attention to quality assessment and human judgements. The course has five lectures on Language Graphs, Graph Clustering, Graph Embeddings, Knowledge Graphs, and Evaluation. They elaborately go through the essential algorithms step-by-step, discuss case studies, and suggest insightful references and datasets. The target audience is undergraduate and graduate students, data analysts, and interdisciplinary researchers (but it is not limited to them). The course was held in person in August 2022 at the 33rd European Summer School in Logic, Language and Information (ESSLLI 2022) in Galway, Ireland: https://2022.esslli.eu/courses-workshops-accepted/week-1-and-2-schedule.html