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Automatically Ordering Events and Times in Text
Údaje o názvu Automatically Ordering Events and Times in Text [electronic resource] / by Leon R.A. Derczynski. Nakladatel Cham : Springer International Publishing : Imprint: Springer, 2017. Fyz.popis XXI, 205 p. 25 illus. online resource. ISBN 9783319472416 Edice Studies in Computational Intelligence, ISSN 1860-949X ; 677 Úplný obsah Introduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion. Poznámky k dostupnosti Přístup pouze pro oprávněné uživatele Dal.odpovědnost SpringerLink (Online service) Předmět.hesla Engineering. * Artificial intelligence. * Text processing (Computer science). * Computational linguistics. * Computational intelligence. Forma, žánr elektronické knihy electronic books Země vyd. Německo Jazyk dok. angličtina Druh dok. Elektronické knihy URL Plný text pro studenty a zaměstnance UPOL kniha
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.
Introduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion.
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