Overview
- Includes recent research on temporal information extraction
- Is devoted to information extraction, spatiotemporal semantics, and handling noisy linguistic data
- Presents methods to automatically order events and times in linguistic data
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 677)
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About this book
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 theoryand 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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Keywords
Table of contents (7 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Automatically Ordering Events and Times in Text
Authors: Leon R.A. Derczynski
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-47241-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-47240-9Published: 26 October 2016
Softcover ISBN: 978-3-319-83688-1Published: 22 April 2018
eBook ISBN: 978-3-319-47241-6Published: 18 October 2016
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXI, 205
Number of Illustrations: 25 b/w illustrations
Topics: Computational Intelligence, Artificial Intelligence, Natural Language Processing (NLP), Computational Linguistics