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Natural language processing for corpus linguistics
紀錄類型 ： 書目-語言資料,印刷品: 單行本
正題名/作者 ： Natural language processing for corpus linguistics/ Jonathan Dunn.
作者 ： Dunn, Jonathan.
出版者 ： Cambridge :Cambridge University Press,2022.
面頁冊數 ： 84 p. :ill., digital ;24 cm.
附註 ： Title from publisher's bibliographic system (viewed on 04 Mar 2022).
標題 ： Corpora (Linguistics) - Data processing. -
電子資源 ： https://doi.org/10.1017/9781009070447
ISBN ： 9781009070447
ISBN ： 9781009074438
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490 1 $aCambridge elements. Elements in corpus linguistics,$x2632-8097
500 $aTitle from publisher's bibliographic system (viewed on 04 Mar 2022).
520 $aCorpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.
650 0$aCorpora (Linguistics)$xData processing.$3292750
650 0$aNatural language processing (Computer science)$3292775
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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:
292750
Corpora (Linguistics)
--Data processing.
LC Class. No.: P128.C68 / D86 2022
Dewey Class. No.: 410.188
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:
292750
Corpora (Linguistics)
--Data processing.
LC Class. No.: P128.C68 / D86 2022
Dewey Class. No.: 410.188
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