Найти книгу: "Recognizing Textual Entailment"

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

A clearer, more accurate performance management strategy Over the past two decades, performance measurement has profoundly changed societies, organizations and the way we live and work. We can now access incredible quantities of data, display, review and report complex information in real time, and monitor employees and processes in detail. But have all these investments in collecting, analysing and reporting data helped companies, governments and people perform better? Measurement Madness is an engaging read, full of anecdotes so peculiar you'll hardly believe them. Each one highlights a performance measurement initiative that went wrong, explains why and – most importantly – shows you how to avoid making the same mistake yourself. The dangers of poorly designed performance measurement are numerous, and even the best how-to guides don't explain how to avoid them. Measurement Madness fills in the gap, showing how to ensure you’re measuring the right things, rewarding the behaviours that deserve rewarding, and interpreting results in a way that will improve things rather than complicate them. This book will help you to recognize, correct and even avoid common performance measurement problems, including: Measuring for the sake of measuring Assuming that measurement is an instant fix for performance issues Comparing sets of data that have nothing in common and hoping to learn something Using targets and rewards to promote certain behaviours, and achieving exactly the opposite ones. Reading Measurement Madness will enable you to design a simple, effective performance measurement system, which will have the intended result of creating value in your organization.

All too often, simple acts of human kindness are often overlooked and under utilized by people in leadership roles. Advising mutual respect and recognition of accomplishments, Encouraging the Heart shows us how true leaders encourage and motivate those they work with by helping them find their voice and making them feel like heroes. Recognized experts in the field of leadership, authors James Kouzes and Barry Posner show us that, through love, leaders can encourage, and indeed allow those around them to be their very best. Both practical and inspirational, Encouraging the Heart gives readers a thoughtful approach to motivating individuals within an organizational structure. Read Chapter 3 or Chapter 12, or see The Encouragement Index.

This book provides guidance on characterizing, recognizing, and responding to warning signs to help avoid process incidents and injuries before they occur. The guidance can be used by both process safety management (PSM) professionals in evaluating their processes and PSM systems as well as for operators who are often the frontline defense against process incidents. Warning signs may consist of process deviations or upsets, instrumentation warnings or alarms, past operating history and incidents, observable problems such as corrosion or unusual odors, audit results indicating procedures are not being followed, or a number of other indicators. Filled with photos and practical tips, this book will turn anyone in a process plant into a hazard lookout and will help prevent potential incidents before they turn into catastrophic events.

This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access: – information extraction and retrieval; – text classification and clustering; – opinion mining; – comprehension aids (automatic summarization, machine translation, visualization). In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications concerned, by highlighting the relationship between models and applications and by illustrating the behavior of each model on real collections. Textual Information Access is organized around four themes: informational retrieval and ranking models, classification and clustering (regression logistics, kernel methods, Markov fields, etc.), multilingualism and machine translation, and emerging applications such as information exploration. Contents Part 1: Information Retrieval 1. Probabilistic Models for Information Retrieval, Stephane Clinchant and Eric Gaussier. 2. Learnable Ranking Models for Automatic Text Summarization and Information Retrieval, Massih-Reza Amini, David Buffoni, Patrick Gallinari,?Tuong Vinh Truong and Nicolas Usunier. Part 2: Classification and Clustering 3. Logistic Regression and Text Classification, Sujeevan Aseervatham, Eric Gaussier, Anestis Antoniadis,?Michel Burlet and Yves Denneulin. 4. Kernel Methods for Textual Information Access, Jean-Michel Renders. 5. Topic-Based Generative Models for Text Information Access, Jean-Cedric Chappelier. 6. Conditional Random Fields for Information Extraction, Isabelle Tellier and Marc Tommasi. Part 3: Multilingualism 7. Statistical Methods for Machine Translation, Alexandre Allauzen and Francois Yvon. Part 4: Emerging Applications 8. Information Mining: Methods and Interfaces for Accessing Complex Information, Josiane Mothe, Kurt Englmeier and Fionn Murtagh. 9. Opinion Detection as a Topic Classification Problem, Juan-Manuel Torres-Moreno, Marc El-Beze, Patrice Bellot and?Frederic Bechet.

The Wiley Handbook of Contextual Behavioral Science describes the philosophical and empirical foundation of the contextual behavioral science movement; it explores the history and goals of CBS, explains its core analytic assumptions, and describes Relational Frame Theory as a research and practice program. This is the first thorough examination of the philosophy, basic science, applied science, and applications of Contextual Behavioral Science Brings together the philosophical and empirical contributions that CBS is making to practical efforts to improve human wellbeing Organized and written in such a way that it can be read in its entirety or on a section-by-section basis, allowing readers to choose how deeply they delve into CBS Extensive coverage of this wide ranging and complex area that encompasses both a rich basic experimental tradition and in-depth clinical application of that experimental knowledge Looks at the development of RFT, and its implications for alleviating human suffering