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Domain-Sensitive Temporal Tagging (Paperback)
Loot Price: R1,318
Discovery Miles 13 180
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Domain-Sensitive Temporal Tagging (Paperback)
Series: Synthesis Lectures on Human Language Technologies
Expected to ship within 10 - 15 working days
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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.
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