Lectures related to Taxonomy Induction: Relations Extraction and Graph Construction

Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Explores information extraction using classifiers, features, and syntactic analysis.

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Explores taxonomy induction, learning terms and relationships to construct hierarchical structures.

Covers the presentation of 4 projects in the course and related logistics.