Automatically ordering events and times in text /
By: Derczynski, Leon R. A
Material type: BookSeries: Studies in computational intelligence ; 677.Publisher: Cham : Springer International Publishing : Imprint: Springer, c2017.Description: xxi, 205 p. : ill. ; 24 cm.ISBN: 9783319472409Subject(s): Engineering | Artificial intelligence | Text processing (Computer science) | Computational linguistics | Computational intelligenceDDC classification: 006.3 DE AU Online resources: Location MapItem type | Home library | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|
REGULAR | University of Wollongong in Dubai Main Collection | 006.3 DE AU (Browse shelf) | Available | T0011588 |
, Shelving location: Main Collection Close shelf browser
006.3 CO AR Artificial intelligence illuminated / | 006.3 CO MP Computational intelligence for decision support in cyber-physical systems / | 006.3 CO MP Computational intelligence for multimedia big data on the cloud with engineering applications | 006.3 DE AU Automatically ordering events and times in text / | 006.3 DU DA Data mining : | 006.3 ER IN Introduction to artificial intelligence | 006.3 ER IN Introduction to artificial intelligence |
Introduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion.
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 theory and 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.