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Time expression and named entity rec...

Zhong, Xiaoshi.

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Time expression and named entity recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time expression and named entity recognition/ by Xiaoshi Zhong, Erik Cambria.
Author:	Zhong, Xiaoshi.
other author:	Cambria, Erik.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xix, 96 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Literature Review -- Chapter 3. Data Analysis -- Chapter 4. SynTime: Token Types and Heuristic Rules -- 5. TOMN: Constituent-based Tagging Scheme -- Chapter 6. UGTO: Uncommon Words and Proper Nouns -- Chapter 7. Conclusion and Future Work.
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-030-78961-9
ISBN:	9783030789619

Time expression and named entity recognition
Zhong, Xiaoshi.

Time expression and named entity recognition[electronic resource] /by Xiaoshi Zhong, Erik Cambria. - Cham :Springer International Publishing :2021. - xix, 96 p. :ill. (some col.), digital ;24 cm. - Socio-affective computing,v.102509-5714 ;. - Socio-affective computing ;v.10..

Chapter 1. Introduction -- Chapter 2. Literature Review -- Chapter 3. Data Analysis -- Chapter 4. SynTime: Token Types and Heuristic Rules -- 5. TOMN: Constituent-based Tagging Scheme -- Chapter 6. UGTO: Uncommon Words and Proper Nouns -- Chapter 7. Conclusion and Future Work.

This book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use.

ISBN: 9783030789619

Standard No.: 10.1007/978-3-030-78961-9doiSubjects--Topical Terms:

565309
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Time expression and named entity recognition
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100 1 $a Zhong, Xiaoshi. $3 3513830
245 1 0 $a Time expression and named entity recognition $h [electronic resource] / $c by Xiaoshi Zhong, Erik Cambria.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xix, 96 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Socio-affective computing, $x 2509-5714 ; $v v.10
505 0 $a Chapter 1. Introduction -- Chapter 2. Literature Review -- Chapter 3. Data Analysis -- Chapter 4. SynTime: Token Types and Heuristic Rules -- 5. TOMN: Constituent-based Tagging Scheme -- Chapter 6. UGTO: Uncommon Words and Proper Nouns -- Chapter 7. Conclusion and Future Work.
520 $a This book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use.
650 0 $a Natural language processing (Computer science) $3 565309
650 1 4 $a Artificial Intelligence. $3 769149
700 1 $a Cambria, Erik. $3 2165436
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Socio-affective computing ; $v v.10. $3 3513831
856 4 0 $u https://doi.org/10.1007/978-3-030-78961-9
950 $a Biomedical and Life Sciences (SpringerNature-11642)