Livre: Dagan etal, Recognizing Textual Entailment

[Thierry Hamon]

Date: Mon, 21 Apr 2014 08:52:36 +1000
From: Graeme Hirst <gh at cs.toronto.edu>
Message-Id: <726D4AFF-48F4-4053-8C53-0EAF3ED36035 at cs.toronto.edu>
X-url: http://dx.doi.org/10.2200/S00509ED1V01Y201305HLT023


BOOK ANNOUNCEMENT

Recognizing Textual Entailment: Models and Applications

by Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto

Synthesis Lectures on Human Language Technologies #23 (Morgan & Claypool
Publishers), 2013, 220 pages

ABSTRACT:

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

http://dx.doi.org/10.2200/S00509ED1V01Y201305HLT023


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