Dragomir Radev and colleague author new book

More information: Cambridge University Press webpage with browseable excerpts and purchase information Amazon webpage with purchase information

Professor Dragomir Radev and Rada Mihalcea, associate professor of computer science at the University of North Texas, have co-authored a new book entitled “Graph-Based Natural Language Processing and Information Retrieval,” which has been published by Cambridge University Press.

The book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. Readers will come away with a firm understanding of the major methods and applications of these topics that rely on graph-based representations and algorithms.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks.

Prof. Radev is a member of the Artificial Intelligence Lab in CSE and is also on the faculty at the School of Information. His research interests include Information retrieval, natural language processing, digital libraries, text and data mining, and artificial intelligence.

Prof. Mihalcea heads the Language and Information Technologies Group at the University of North Texas. Her research interests are in natural language processing, machine learning, and information retrieval.