Domain-Sensitive Temporal Tagging

Domain-Sensitive Temporal Tagging

Jannik Stroetgen, Michael Gertz
ISBN: 9781627054591 | PDF ISBN: 9781627054997
Copyright © 2016 | 152 Pages | Publication Date: July, 2016

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This book covers the topic of temporal tagging, the detection of temporal expressions and the normalizationof their semantics to some standard format. It places a special focus on the challenges and opportunities of domain-sensitive temporal tagging. After providing background knowledge on the concept of time, the book continues with a comprehensive survey of current research on temporal tagging. The authors provide an overview of existing techniques and tools, and highlight key issues that need to be addressed. This book is a valuable resource for researchers and application developers who need to become familiar with the topic and want to know the recent trends, current tools and techniques, as well as different application domains in which temporal information is of utmost importance.

Due to the prevalence of temporal expressions in diverse types of documents and the importance of temporal information in any information space, temporal tagging is an important task in natural language processing (NLP), and applications of several domains can benefit from the output of temporal taggers to provide more meaningful and useful results.

In the last few years, temporal tagging has been an active field in NLP and computational linguistics. Several approaches to temporal tagging have been proposed, annotation standards have been developed, gold standard data sets have been created, and research competitions have been organized. Furthermore, some temporal taggers have also been made publicly available so that temporal tagging output is not just exploited in research, but is finding its way into real world applications. In addition, this book particularly focuses on domain-specific temporal tagging of documents. This is a crucial aspect as different types of documents (e.g., news articles, narratives, and colloquial texts) result in diverse challenges for temporal taggers and should be processed in a domain-sensitive manner.

Table of Contents

List of Figures
List of Tables
The Concept of Time
Foundations of Temporal Tagging
Domain-sensitive Temporal Tagging
Techniques and Tools
Summary and Future Research Directions
Authors' Biographies

About the Author(s)

Jannik Stroetgen, Max Planck Institute for Informatics
Jannik Stroetgen is a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrucken, Germany. He studied computational linguistics and economics at Heidelberg University, Germany and received his Magister Artium in 2009. Between 2009 and 2015, he worked as a research assistant at the Computer Science department in Heidelberg. In March 2015, he defended his Ph.D. thesis, in which he worked on temporal, geographic, and event-centric information extraction and retrieval supervised by Prof. Dr. Michael Gertz. In the context of his thesis, the domain-sensitive and multilingual temporal tagger HeidelTime has been developed. It was made publicly available and is constantly improved.

Michael Gertz, Heidelberg University
Michael Gertz is a full professor at Heidelberg University where he heads the database systems group at the faculty of Mathematics and Computer Science. He received his diploma in Computer Science from the University of Dortmund, Germany, and his Ph.D. from the University of Hanover, Germany, in 1996. From 1997 until 2008 he was on the faculty at the University of California at Davis. He currently serves on the editorial board of the ACM Transactions on Spatial Algorithms and Systems, and he is an associate editor of the ACM Journal on Data and Information Quality. His research interests include the management and analysis of temporal, spatial, and spatio-temporal data, data mining, text mining, and social network analysis.


This gives a strong and far-reaching catalogue of temporal expression resources and tools, more than I've ever seen gathered in one place before, with an objective and subjective description of each. This makes it better than a reference resource, guiding the reader through where the author thinks resources will be useful. Some of the opinions aren't ones that would come to me instinctively, but even in these cases, there is always a well-explained point in the book to support the information here, and everything is quite principled. All in all, this makes for a comprehensive reference work, well-organised and accessible to masters students or even final-year bachelors students, with useful concepts explained from the ground up. An excellent book in the area. Now I need an event extraction one and temporal ordering one to accompany it on the office shelf.
Natural Language Engineering 23 (3): 481–483.

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