Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1613-9658
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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online resource.
- Erschienen in
-
Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data ; day:9 ; month:7 ; year:2022 ; pages:1-28
Computational statistics ; (9.7.2022), 1-28
- Urheber
-
Weisser, Christoph
Gerloff, Christoph
Thielmann, Anton
Python, Andre
Reuter, Arik
Kneib, Thomas
Säfken, Benjamin
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s00180-022-01246-z
- URN
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urn:nbn:de:101:1-2022083123235178935166
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:35 MESZ
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Beteiligte
- Weisser, Christoph
- Gerloff, Christoph
- Thielmann, Anton
- Python, Andre
- Reuter, Arik
- Kneib, Thomas
- Säfken, Benjamin
- SpringerLink (Online service)
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