Automatically Ordering Events and Times in Text (Hardcover, 1st ed. 2017)

The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally. This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties. It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 677
Release date:	October 2016
First published:	2017
Authors:	Leon R.A. Derczynski
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Hardcover
Pages:	205
Edition:	1st ed. 2017
ISBN-13:	978-3-319-47240-9
Categories:	Books > Language & Literature > Language & linguistics > Computational linguistics Books > Computing & IT > Computer programming > Programming languages > General Books > Computing & IT > Computer software packages > Desktop publishing software > General Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
LSN:	3-319-47240-2
Barcode:	9783319472409

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