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 met...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2022
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| Schriftenreihe: | Cambridge elements. Elements in corpus linguistics
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| Schlagworte: | |
| Zusammenfassung: | "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." Klappentext |
| Beschreibung: | 84 Seiten Illustrationen, Diagramme, Karten 22.9 cm x 15.2 cm |
| ISBN: | 9781009074438 |
Internformat
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| 520 | 3 | |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." Klappentext | |
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Datensatz im Suchindex
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| author | Dunn, Jonathan ca. 20./21. Jh |
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| id | DE-604.BV048283670 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:01:08Z |
| indexdate | 2024-08-21T00:50:53Z |
| institution | BVB |
| isbn | 9781009074438 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033663826 |
| oclc_num | 1337130562 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | 84 Seiten Illustrationen, Diagramme, Karten 22.9 cm x 15.2 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Cambridge University Press |
| record_format | marc |
| series2 | Cambridge elements. Elements in corpus linguistics |
| spelling | Dunn, Jonathan ca. 20./21. Jh. Verfasser (DE-588)1256969044 aut Natural language processing for corpus linguistics Jonathan Dunn (University of Canterbury) Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore Cambridge University Press 2022 84 Seiten Illustrationen, Diagramme, Karten 22.9 cm x 15.2 cm txt rdacontent n rdamedia nc rdacarrier Cambridge elements. Elements in 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 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." Klappentext 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 Online-Ausgabe 978-1-00-907044-7 |
| spellingShingle | Dunn, Jonathan ca. 20./21. Jh Natural language processing for corpus linguistics 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 (University of Canterbury) |
| title_fullStr | Natural language processing for corpus linguistics Jonathan Dunn (University of Canterbury) |
| title_full_unstemmed | Natural language processing for corpus linguistics Jonathan Dunn (University of Canterbury) |
| title_short | Natural language processing for corpus linguistics |
| title_sort | natural language processing for corpus linguistics |
| topic | Automatische Sprachanalyse (DE-588)4129935-8 gnd Korpus Linguistik (DE-588)4165338-5 gnd |
| topic_facet | Automatische Sprachanalyse Korpus Linguistik |
| work_keys_str_mv | AT dunnjonathan naturallanguageprocessingforcorpuslinguistics |