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| dc.contributor | Vitrià i Marca, Jordi | |
| dc.creator | Julià Carrillo, Oriol | |
| dc.date | 2017-04-06T09:14:08Z | |
| dc.date | 2017-04-06T09:14:08Z | |
| dc.date | 2016-06-26 | |
| dc.date.accessioned | 2024-12-16T10:24:04Z | |
| dc.date.available | 2024-12-16T10:24:04Z | |
| dc.identifier | http://hdl.handle.net/2445/109443 | |
| dc.identifier.uri | http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/17048 | |
| dc.description | Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Jordi Vitrià i Marca | |
| dc.description | Latent Dirichlet Allocation (LDA) are a suite of algorithms that are often used for topic modeling. We study the statistical model behind LDA and review how tensor methods can be used for learning LDA, as well as implement a variation of an already existing method. Next, we present an innovative algorithm for temporal topic modeling and provide a new dataset for learning topic models over time. Last, we create a visualization for the word-topic probabilities. | |
| dc.format | 59 p. | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.rights | cc-by-nc-nd (c) Oriol Julià Carrillo, 2016 | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/es | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | Treballs Finals de Grau (TFG) - Matemàtiques | |
| dc.subject | Tractament del llenguatge natural (Informàtica) | |
| dc.subject | Treballs de fi de grau | |
| dc.subject | Aprenentatge automàtic | |
| dc.subject | Probabilitats | |
| dc.subject | Algorismes computacionals | |
| dc.subject | Natural language processing (Computer science) | |
| dc.subject | Bachelor's theses | |
| dc.subject | Machine learning | |
| dc.subject | Probabilities | |
| dc.subject | Computer algorithms | |
| dc.title | A tensor based approach for temporal topic modeling | |
| dc.type | info:eu-repo/semantics/bachelorThesis |
| Fitxers | Grandària | Format | Visualització |
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