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  • Book
  • © 2017

Automatically Ordering Events and Times in Text

  • 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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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xxi
  2. Introduction

    • Leon R. A. Derczynski
    Pages 1-8
  3. Events and Times

    • Leon R. A. Derczynski
    Pages 9-24
  4. Temporal Relations

    • Leon R. A. Derczynski
    Pages 25-61
  5. Relation Labelling Analysis

    • Leon R. A. Derczynski
    Pages 63-84
  6. Using Temporal Signals

    • Leon R. A. Derczynski
    Pages 85-137
  7. Using a Framework of Tense and Aspect

    • Leon R. A. Derczynski
    Pages 139-177
  8. Conclusion

    • Leon R. A. Derczynski
    Pages 179-185
  9. Back Matter

    Pages 187-205

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.

Authors and Affiliations

  • Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom

    Leon R.A. Derczynski

Bibliographic Information

Buy it now

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access