Graph-based Algorithms for Natural Language Processing and Information Retrieval

Graph theory is a well studied discipline, and so are the fields of natural language processing and information retrieval. However, most of the times, they are perceived as different disciplines, with different algorithms, different applications, and different potential end-users. The goal of this tutorial is to provide an overview of methods and applications in natural language processing and information retrieval that rely on graph-based algorithms. This will include techniques for graph traversal, minimum path length, min-cut algorithms, minimum spanning trees, random walks, etc. and their application to information retrieval and Web search, text understanding (word sense disambiguation and semantic classes), parsing, text summarization, keyword extraction, text clustering, and others.