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memoria.pdf (9.88 MB)

Document type

Bachelor thesis

Publication date

2016-06-26

Publication license

cc-by-nc-nd (c) Oriol Julià Carrillo, 2016
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/109443

A tensor based approach for temporal topic modeling

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Abstract

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.

Description

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Jordi Vitrià i Marca

Subject

Tractament del llenguatge natural (Informàtica), Treballs de fi de grau, Aprenentatge automàtic, Probabilitats, Algorismes computacionals

Subject (English)

Natural language processing (Computer science), Bachelor's theses, Machine learning, Probabilities, Computer algorithms

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Treballs Finals de Grau (TFG) - Matemàtiques

Citation

JULIÀ CARRILLO, Oriol. A tensor based approach for temporal topic modeling. [consulted: 10 of August of 2026]. Available at: https://hdl.handle.net/2445/109443

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