Networks and Natural Language Processing

  • Dragomir R. Radev University of Michigan
  • Rada Mihalcea University of North Texas


Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

Author Biographies

Dragomir R. Radev, University of Michigan

Department of Electrical Engineering and Computer Science

Associate Professor

Rada Mihalcea, University of North Texas

Department of Computer Science

Associate Professor