Automatic Text Categorization
From Information Retrieval to Support Vector Learning

Area 09 – Ingegneria industriale e dell'informazione

This book introduces Automatic Text Categorization by sketching the recent theoretical results in the area of Statistical Learning Theory and their immediate consequences in the technology of Support Vector Learning. The ideas and the empirical evidence contained in this book have originated from years of research practice. In particular, the foundations of Automatic Text Categorization (as discussed in this book) can be applied to several other Artificial Intelligence (AI) areas, making it appealing for any scholar in Natural Language Processing, Information Retrieval, Computational Linguistics, Data Mining and Machine Learning. This book will not answer all questions about the learnability of AI phenomena, but some of the presented results (i.e. successes and failures) can play a reference and inspiration role for many other open problems focused on the automatic learning of Natural Language.Roberto Basili is Associate Professor of Computer Science at the University of Rome “Tor Vergata” where he carries out research and development activities on Natural Language Processing and Machine Learning. He received his degree in Mathematics in 1989 from the University of Rome “La Sapienza” and his PhD in Computer Science at the Department of Electronic Engineering at “Tor Vergata” University in 1993. His research interests include Representation and Learning of Lexical Knowledge, Information Retrieval, Information Extraction and Ontology Engineering. He is author of more than 80 papers on international journals, books and conference proceedings.Alessandro Moschitti graduated in 1998 from the University of Rome “La Sapienza” with a Master Degree in Computer Science. He is currently a researcher at the Computer Science Department of the University of Rome “Tor Vergata”, where, in 2003, he obtained his PhD in Computer Science. Between 2002 and 2004, he worked as an associate researcher at the University of Texas at Dallas. His expertise concerns machine learning approaches to Natural Language Processing and Information Retrieval. He has recently devised innovative kernels within Support Vector and other kernel-based machines for advanced semantic processing.

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