Natural language processing for corpus linguistics:
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 a...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2022
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| Series: | Cambridge elements
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| Subjects: | |
| Online Access: | DE-12 DE-473 Volltext |
| Summary: | 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 |
| Item Description: | Title from publisher's bibliographic system (viewed on 04 Mar 2022) |
| Physical Description: | 1 Online-Ressource (84 Seiten) |
| ISBN: | 9781009070447 |
| DOI: | 10.1017/9781009070447 |
Staff View
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| 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 | ||
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Record in the Search Index
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| author | Dunn, Jonathan ca. 20./21. Jh |
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| author_facet | Dunn, Jonathan ca. 20./21. Jh |
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| dewey-full | 410.188 |
| dewey-hundreds | 400 - Language |
| dewey-ones | 410 - Linguistics |
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| discipline | Sprachwissenschaft Anglistik / Amerikanistik Literaturwissenschaft |
| discipline_str_mv | Sprachwissenschaft Anglistik / Amerikanistik Literaturwissenschaft |
| doi_str_mv | 10.1017/9781009070447 |
| format | Electronic eBook |
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| id | DE-604.BV048203122 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:47:11Z |
| indexdate | 2024-08-21T00:50:53Z |
| institution | BVB |
| isbn | 9781009070447 |
| language | English |
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| spelling | Dunn, Jonathan ca. 20./21. Jh. (DE-588)1256969044 aut Natural language processing for corpus linguistics Jonathan Dunn Cambridge Cambridge University Press 2022 1 Online-Ressource (84 Seiten) txt rdacontent c rdamedia cr rdacarrier Cambridge elements 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 Corpora (Linguistics) / Data processing Natural language processing (Computer science) Automatische Sprachanalyse (DE-588)4129935-8 gnd rswk-swf Korpus Linguistik (DE-588)4165338-5 gnd rswk-swf Korpus Linguistik (DE-588)4165338-5 s Automatische Sprachanalyse (DE-588)4129935-8 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-00-907443-8 https://doi.org/10.1017/9781009070447 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Dunn, Jonathan ca. 20./21. Jh Natural language processing for corpus linguistics Corpora (Linguistics) / Data processing Natural language processing (Computer science) Automatische Sprachanalyse (DE-588)4129935-8 gnd Korpus Linguistik (DE-588)4165338-5 gnd |
| subject_GND | (DE-588)4129935-8 (DE-588)4165338-5 |
| title | Natural language processing for corpus linguistics |
| title_auth | Natural language processing for corpus linguistics |
| title_exact_search | Natural language processing for corpus linguistics |
| title_exact_search_txtP | Natural language processing for corpus linguistics |
| title_full | Natural language processing for corpus linguistics Jonathan Dunn |
| title_fullStr | Natural language processing for corpus linguistics Jonathan Dunn |
| title_full_unstemmed | Natural language processing for corpus linguistics Jonathan Dunn |
| title_short | Natural language processing for corpus linguistics |
| title_sort | natural language processing for corpus linguistics |
| topic | Corpora (Linguistics) / Data processing Natural language processing (Computer science) Automatische Sprachanalyse (DE-588)4129935-8 gnd Korpus Linguistik (DE-588)4165338-5 gnd |
| topic_facet | Corpora (Linguistics) / Data processing Natural language processing (Computer science) Automatische Sprachanalyse Korpus Linguistik |
| url | https://doi.org/10.1017/9781009070447 |
| work_keys_str_mv | AT dunnjonathan naturallanguageprocessingforcorpuslinguistics |