Topic model tutorial
| Autor: | Lisa Posch, Christoph Carl Kling, Arnim Bleier, Laura Dietz |
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| Rok vydání: | 2016 |
| Předmět: |
Topic model
business.industry Computer science 05 social sciences Probabilistic logic 010501 environmental sciences computer.software_genre 01 natural sciences Latent Dirichlet allocation Dirichlet distribution Dynamic topic model symbols.namesake 0502 economics and business symbols Artificial intelligence Probabilistic topic modeling Graphical model 050207 economics business computer Natural language processing 0105 earth and related environmental sciences Gibbs sampling |
| Zdroj: | WebSci |
| Popis: | In this tutorial, we teach the intuition and the assumptions behind topic models. Topic models explain the co-occurrences of words in documents by extracting sets of semantically related words, called topics. These topics are semantically coherent and can be interpreted by humans. Starting with the most popular topic model, Latent Dirichlet Allocation (LDA), we explain the fundamental concepts of probabilistic topic modeling. We organise our tutorial as follows: After a general introduction, we will enable participants to develop an intuition for the underlying concepts of probabilistic topic models. Building on this intuition, we cover the technical foundations of topic models, including graphical models and Gibbs sampling. We conclude the tutorial with an overview on the most relevant adaptions and extensions of LDA. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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