TY  - BOOK
AU  - Strötgen,Jannik
AU  - Gertz,Michael
TI  - Domain-sensitive temporal tagging
T2  - Synthesis lectures on human language technologies
SN  - 9781627054997
AV  - QA 76.9.N38 S77.2016
PY  - 2016///
CY  - [San Rafael, California]
PB  - Morgan & Claypool Publishers
KW  - Natural language processing (Computer science)
KW  - Procesamiento de lenguaje natural (Computación)
KW  - Computational linguistics
KW  - Lingüística computacional
KW  - Domain-specific programming languages
KW  - Lenguajes especificos de dominio(Computación)
N1  - Incluye referencias bibliográficas (páginas 113-126) e índice; 1. Introduction -- 1.1 The task of temporal tagging -- 1.2 Application examples exploiting temporal tagging -- 1.3 Summary of the chapter; 2. The concept of time -- 2.1 Key characteristics of temporal information -- 2.2 Temporal expressions in documents -- 2.3 Realizations of temporal expressions -- 2.4 Summary of the chapter; 3. Foundations of temporal tagging -- 3.1 Annotation standards -- 3.2 Evaluating temporal taggers -- 3.3 Research competitions -- 3.4 Annotated news-style corpora -- 3.5 Summary of the chapter; 4. Domain-sensitive temporal tagging -- 4.1 Temporal tagging of news-style documents -- 4.2 The concept of a domain -- 4.3 Annotated non-news-style corpora -- 4.4 Characteristics of different domains -- 4.4.1 News-style documents -- 4.4.2 Narrative-style documents -- 4.4.3 Colloquial-style documents -- 4.4.4 Autonomic-style documents -- 4.4.5 Further domains and summary -- 4.5 Comparative corpus analysis, challenges for domain-sensitive temporal tagging -- 4.6 Strategies for domain-sensitive temporal tagging -- 4.7 Summary of the chapter; 5. Techniques and tools -- 5.1 Overview of approaches to temporal tagging -- 5.2 Overview of existing temporal taggers -- 5.2.1 TIPSem, exploiting semantic information -- 5.2.2 HeidelTime, rule-based, multilingual, domain-sensitive -- 5.2.3 SUTime, rule-based, Stanford CoreNLP component -- 5.2.4 UWTime, semantic parsing of time expressions -- 5.2.5 Comparison and evaluation results -- 5.3 The value of domain-sensitive temporal tagging -- 5.4 Approaches to subtasks and related tasks -- 5.5 Highly multilingual temporal tagging -- 5.6 Summary of the chapter; 6. Summary and future research directions -- 6.1 Summary -- 6.2 Future directions -- Bibliography -- Authors' biographies -- Index
N2  - 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
ER  -