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Domain-Sensitive Temporal Tagging

Synthesis Lectures on Human Language Technologies

Jannik Strötgen​‌
Max Planck Institute for Informatics, Saarbrücken, Germany
Michael Gertz​‌
Heidelberg University, Germany


This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of 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 recent 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 / Preface / Acknowledgments / Introduction / The Concept of Time / Foundations of Temporal Tagging / Domain-sensitive Temporal Tagging / Techniques and Tools / Summary and Future Research Directions / Bibliography / Authors' Biographies / Index

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Cited by

Mari Sato, Adam Jatowt, Yijun Duan, Ricardo Campos, Masatoshi Yoshikawa​‌. (2021) Estimating Contemporary Relevance of Past News. 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 70-79.
Chris R. Giannella, Ransom K. Winder, Joseph P. Jubinski​‌. (2019) Annotation projection for temporal information extraction. Natural Language Engineering 25:3, 385-403.
Online publication date: 15-May-2019.
Sabyasachi Kamila, Mohammad Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya​‌. (2019) Tempo-HindiWordNet. ACM Transactions on Asian and Low-Resource Language Information Processing 18:2, 1-22.
Online publication date: 10-Feb-2019.
María Navas-Loro, Erwin Filtz, Víctor Rodríguez-Doncel, Axel Polleres, Sabrina Kirrane​‌. (2019) TempCourt: evaluation of temporal taggers on a new corpus of court decisions. The Knowledge Engineering Review 34.
Online publication date: 17-Dec-2019.
Susanna Abraham, Stephan Mäs, Lars Bernard​‌. (2018) Extraction of spatio-temporal data about historical events from text documents. Transactions in GIS 22:3, 677-696.
Online publication date: 17-Aug-2018.
Johanna Geiß, Andreas Spitz, Michael Gertz​‌. 2018. , 115.
Rafael Faria de Azevedo, João Pedro Santos Rodrigues, Mayara Regina da Silva Reis, Claudia Maria Cabral Moro, Emerson Cabrera Paraiso​‌. 2018. Temporal Tagging of Noisy Clinical Texts in Brazilian Portuguese. Computational Processing of the Portuguese Language, 231-241.
Tarik Boudaa, Mohamed El Marouani, Nourddine Enneya​‌. 2018. Arabic Temporal Expression Tagging and Normalization. Big Data, Cloud and Applications, 546-557.
Leon R. A. Derczynski​‌. 2017. Events and Times. Automatically Ordering Events and Times in Text, 9-24.

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