Recognizing Textual Entailment

Recognizing Textual Entailment
Models and Applications

Ido Dagan, Dan Roth, Fabio Massimo Zanzotto, Mark Sammons
ISBN: 9781598298345 | PDF ISBN: 9781598298352
Copyright © 2013 | 220 Pages | Publication Date: 07/01/2013

BEFORE YOU ORDER: You may have Academic or Corporate access to this title. Click here to find out: 10.2200/S00509ED1V01Y201305HLT023

Ordering Options: Paperback $45.00   E-book $36.00   Paperback & E-book Combo $56.25


Why pay full price? Members receive 15% off all orders.
Learn More Here

Read Our Digital Content License Agreement (pop-up)

Purchasing Options:


In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications, from Machine Translation to Semantic Search to Information Extraction. It also avoids commitment to any specific meaning representation and reasoning framework, broadening its appeal within the research community. This level of abstraction also facilitates evaluation, a crucial component of any technological advancement program.

This book explains the RTE task formulation adopted by the NLP research community, and gives a clear overview of research in this area. It draws out commonalities in this research, detailing the intuitions behind dominant approaches and their theoretical underpinnings.

This book has been written with a wide audience in mind, but is intended to inform all readers about the state of the art in this fascinating field, to give a clear understanding of the principles underlying RTE research to date, and to highlight the short- and long-term research goals that will advance this technology.

Table of Contents

List of Figures
List of Tables
Preface
Acknowledgments
Textual Entailment
Architectures and Approaches
Alignment, Classification, and Learning
Case Studies
Knowledge Acquisition for Textual Entailment
Research Directions in RTE
Bibliography
Authors' Biographies

About the Author(s)

Ido Dagan, Bar-Ilan University, Israel
Ido Dagan is an Associate Professor in the Department of Computer Science at Bar-Ilan University, Israel. His interests are in applied semantic processing, focusing on the development of generic textual inference models, knowledge acquisition methods, and novel application schemes that are based on them. Dagan and colleagues defined the textual entailment recognition task and organized the series of Recognizing Textual Entailment Challenges. He was the President of the Association for Computational Linguistics (ACL) in 2010 and served on its Executive Committee during 2008-2011. In that capacity, he led the establishment of the Transactions of the Association for Computational Linguistics journal. Dagan received his B.A. summa cum laude and his Ph.D. (1992) in Computer Science from the Technion. He was a research fellow at the IBM Haifa Scientific Center (1991) and a Member of Technical Staff at AT&T Bell Laboratories (1992-1994). During 1998-2003 he was co-founder and CTO of FocusEngine and VP of Technology of LingoMotors.

Dan Roth, University of Illinois at Urbana-Champaign
Dan Roth is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and the Beckman Institute of Advanced Science and Technology, and a University of Illinois Scholar. He is a fellow of AAAI, the ACL, and the ACM, for his contributions to the foundations of machine learning and inference and for developing learning centered solutions for natural language processing problems. Roth was the program chair of AAAI'11, ACL'03, and CoNLL'02 and has been on the editorial board of several journals in his research areas; he is currently the Associate Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR) and will serve as Editor-in-Chief for a two-year term beginning in 2015. Roth has published broadly in machine learning, natural language processing, knowledge representation, and reasoning and received several paper, teaching, and research awards. Prof. Roth got his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D. in Computer Science from Harvard University in 1995.

Fabio Massimo Zanzotto, University of Rome
Fabio Massimo Zanzotto is an Associate Professor in the Department of Enterprise Engineering of the University of Rome "Tor Vergata." Since 1996, he has been interested in the research endeavor of Artificial Intelligence. He is active in the area of Natural Language Processing, mainly working in three areas: recognizing textual entailment, syntactic parsing for Italian, and, recently, distributed/distributional models for NLP. He is a member of ACL and of the Italian Association on Artificial Intelligence (AIIA). He chaired Brain Informatics 2012 and he reviews for the major conferences and for journals in the areas of NLP and AI.

Mark Sammons, University of Illinois at Urbana-Champaign
Mark Sammons is a Principal Research Scientist working with the Cognitive Computation Group at the University of Illinois. His primary interests are in Natural Language Processing and Machine Learning, with a focus on Textual Entailment and Information Extraction. He has led teams that participated in research projects and shared tasks involving the design and implementation of large end-to-end NLP systems. He has given a tutorial on textual entailment at NAACL in 2010, and written chapters surveying textual entailment research in two books. Mark received his MSC in Computer Science from the University of Illinois in 2004, and his Ph.D. in Mechanical Engineering from the University of Leeds, England, in 2000.

Reviews

Browse by Subject
Case Studies in Engineering
ACM Books
IOP Concise Physics
SEM Books
0 items
LATEST NEWS

Newsletter
Note: Registered customers go to: Your Account to subscribe.

E-Mail Address:

Your Name: