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Recognizing Textual Entailment: Models and Applications (Synthesis Lectures on Human Language Technologies), by Ido Dagan, Dan Roth, Mark
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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
- Sales Rank: #2206016 in Books
- Published on: 2013-07-01
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .50" w x 7.50" l, .86 pounds
- Binding: Paperback
- 220 pages
Most helpful customer reviews
1 of 1 people found the following review helpful.
Clearly written, comprehensible, and comprehensive
By Kevin B. Cohen
Recognizing textual entailment is the task of determining when one piece of text entails or implies the meaning of another: for example, that “She complains of chest discomfort after meals” entails that “She experiences chest discomfort”, but not that “She has been diagnosed with acid reflux disease.” This book describes techniques for using computers to recognize textual entailment. As the book demonstrates, being able to do this is helpful for teaching computers to answer questions, summarize documents, find information, and other things.
The book is exceptionally clearly written, and gives comprehensible explanations of a number of phenomena that make the task difficult, as well as a variety of approaches to performing the task. These include alignment and edit distance, transformations, logical inference, and combinations of these. Recognizing textual entailment requires a variety of different kinds of knowledge, including knowledge about language and knowledge about the world, and a full chapter is devoted to sources of such knowledge. A set of case studies gives detailed descriptions of a number of systems that have been produced to tackle the task, including side-by-side comparisons of the approaches and their results.
The book includes discussions of how to evaluate computer programs that attempt to recognize textual entailment. A number of standard data sets for the task are described in terms of how they were made, what kinds of metrics are most appropriate for measuring performance on them, and what kinds of other tasks they can support.
As mentioned above, the book is quite clearly written, and even a pleasure to read. It offers as much overview as a beginner would need to get started in the field, and as much detail as an expert would need to replicate or extend prior research in their own work.
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