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Automatically ordering events and times in text

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automatically ordering events and times in text/ by Leon R.A. Derczynski.
Author:	Derczynski, Leon R.A.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxi, 205 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion.
Contained By:	Springer eBooks
Subject:	Computational linguistics. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-47241-6
ISBN:	9783319472416

Automatically ordering events and times in text
Derczynski, Leon R.A.

Automatically ordering events and times in text[electronic resource] /by Leon R.A. Derczynski. - Cham :Springer International Publishing :2017. - xxi, 205 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6771860-949X ;. - Studies in computational intelligence ;v.677..

Introduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion.

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.

ISBN: 9783319472416

Standard No.: 10.1007/978-3-319-47241-6doiSubjects--Topical Terms:

606540
Computational linguistics.

LC Class. No.: P98

Dewey Class. No.: 006.35

Automatically ordering events and times in text
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100 1 $a Derczynski, Leon R.A. $3 3217671
245 1 0 $a Automatically ordering events and times in text $h [electronic resource] / $c by Leon R.A. Derczynski.
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505 0 $a Introduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion.
520 $a 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.
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