國立虎尾科技大學 |

Natural language processing for corpus linguistics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Natural language processing for corpus linguistics // Jonathan Dunn.
作者:	Dunn, Jonathan,
面頁冊數:	1 online resource (84 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 04 Mar 2022).
標題:	Natural language processing (Computer science) -
電子資源:	https://doi.org/10.1017/9781009070447
ISBN:	9781009070447 (ebook)

Natural language processing for corpus linguistics /
Dunn, Jonathan,

Natural language processing for corpus linguistics /Jonathan Dunn. - 1 online resource (84 pages) :digital, PDF file(s). - 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: 9781009070447 (ebook)Subjects--Topical Terms:

641811
Natural language processing (Computer science)

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

Dewey Class. No.: 410.188

Natural language processing for corpus linguistics /
LDR:01998nam a2200289 i 4500 001 1122577
003 UkCbUP
005 20220409084158.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2022||||enk o ||1 0|eng|d
020 $a 9781009070447 (ebook)
020 $z 9781009074438 (paperback)
035 $a CR9781009070447
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 4 $a P128.C68 $b D86 2022
082 0 4 $a 410.188 $2 23
100 1 $a Dunn, Jonathan, $e author. $3 1438878
245 1 0 $a Natural language processing for corpus linguistics / $c Jonathan Dunn.
264 1 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a 1 online resource (84 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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 Natural language processing (Computer science) $3 641811
650 0 $a Corpora (Linguistics) $x Data processing. $3 1426741
776 0 8 $i Print version: $z 9781009074438
830 0 $a Cambridge elements. $p Elements in corpus linguistics, $x 2632-8097. $3 1438879
856 4 0 $u https://doi.org/10.1017/9781009070447