| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230829b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319472409 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3 |
| Item number |
DER |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Derczynski, Leon R. A. |
| 245 ## - TITLE STATEMENT |
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| Title |
Automatically ordering events and times in text |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2017 |
| Place of publication, distribution, etc |
Cham : |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxi, 205 p. ; |
| Other physical details |
ill., |
| Dimensions |
25 cm |
| 365 ## - TRADE PRICE |
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| Price amount |
99.99 |
| Price type code |
EUR |
| Unit of pricing |
94.90 |
| 490 ## - SERIES STATEMENT |
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| Series statement |
Studies in Computational Intelligence, |
| Volume number/sequential designation |
1860-949X ; 677 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial Intelligence |
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| Topical term or geographic name as entry element |
Computational Linguistics |
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| Topical term or geographic name as entry element |
Knowledge Representation |
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| Topical term or geographic name as entry element |
Temporal Data Mining |
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| Topical term or geographic name as entry element |
Associationn for computation; |
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| Topical term or geographic name as entry element |
Annotation tools |
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| Topical term or geographic name as entry element |
Computational Linguistics |
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| Topical term or geographic name as entry element |
Cross validation |
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| Topical term or geographic name as entry element |
Difficult links |
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| Topical term or geographic name as entry element |
Error reduction |
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| Topical term or geographic name as entry element |
Gold standard |
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| Topical term or geographic name as entry element |
Function tag |
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| Topical term or geographic name as entry element |
TempEval |
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| Topical term or geographic name as entry element |
Interval logic |
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| Topical term or geographic name as entry element |
Reichenbach's framework |
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| Topical term or geographic name as entry element |
TLINK |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |