國立虎尾科技大學 |

Natural language processing for corpus linguistics

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Natural language processing for corpus linguistics/ Jonathan Dunn.
Author:	Dunn, Jonathan.
Published:	Cambridge :Cambridge University Press, : 2022.,
Description:	84 p. :ill., digital ; : 24 cm.;
Notes:	Title from publisher's bibliographic system (viewed on 04 Mar 2022).
Subject:	Corpora (Linguistics) - Data processing. -
Online resource:	https://doi.org/10.1017/9781009070447
ISBN:	9781009070447

Natural language processing for corpus linguistics
Dunn, Jonathan.

Natural language processing for corpus linguistics[electronic resource] /Jonathan Dunn. - Cambridge :Cambridge University Press,2022. - 84 p. :ill., digital ;24 cm. - Cambridge elements. Elements in corpus linguistics,2632-8097. - Cambridge elements.Elements in corpus linguistics..

Title from publisher's bibliographic system (viewed on 04 Mar 2022).

Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.

ISBN: 9781009070447Subjects--Topical Terms:

1426741
Corpora (Linguistics)
--Data processing.

LC Class. No.: P128.C68 / D86 2022

Dewey Class. No.: 410.188

Natural language processing for corpus linguistics
LDR:01846nam a2200253 a 4500 001 1114099
003 UkCbUP
005 20220409084158.0
006 m d
007 cr nn 008maaau
008 231225s2022 enk o 1 0 eng d
020 $a 9781009070447 $q (electronic bk.)
020 $a 9781009074438 $q (paperback)
035 $a CR9781009070447
040 $a UkCbUP $b eng $c UkCbUP $d GP
050 4 $a P128.C68 $b D86 2022
082 0 4 $a 410.188 $2 23
090 $a P128.C68 $b D923 2022
100 1 $a Dunn, Jonathan. $3 1229841
245 1 0 $a Natural language processing for corpus linguistics $h [electronic resource] / $c Jonathan Dunn.
260 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a 84 p. : $b ill., digital ; $c 24 cm.
490 1 $a Cambridge elements. Elements in corpus linguistics, $x 2632-8097
500 $a Title from publisher's bibliographic system (viewed on 04 Mar 2022).
520 $a Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.
650 0 $a Corpora (Linguistics) $x Data processing. $3 1426741
650 0 $a Natural language processing (Computer science) $3 641811
830 0 $a Cambridge elements. $p Elements in corpus linguistics. $3 1426755
856 4 0 $u https://doi.org/10.1017/9781009070447