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Seminar 4 Probabilistic Topic Model
Содержание
- 1. Seminar 4 Probabilistic Topic Model
- 2. Topic modelingModels of a collection of compositesComposites
- 3. Assumptionssemantic information can be derived from a
- 4. Generative model
- 5. Probabilistic model
- 6. Dirichlet distribution
- 7. Geometric interpretation
- 8. Main goal of the algorithmTo invert the
- 9. Скачать презентанцию
Topic modelingModels of a collection of compositesComposites are documentsParts are words (or phrases, n-grams)Two outputs:chance of selecting a particular part when sampling a particular topic chance of selecting a particular topic when
Слайды и текст этой презентации
Слайд 1
Seminar 4
Probabilistic Topic Model
Mikhail Kamrotov
Data Analysis in Politics and Journalism
Winter/Spring
2019
Слайд 2Topic modeling
Models of a collection of composites
Composites are documents
Parts are
words (or phrases, n-grams)
Two outputs:
chance of selecting a particular part
when sampling a particular topic chance of selecting a particular topic when sampling a particular document or composite
Слайд 3Assumptions
semantic information can be derived from a word-document co-occurrence matrix;
topic
is a probability distribution over words
to make a new
document, one chooses a distribution over topics for each word in that document, one chooses a topic at random according to this distribution, and draws a word from that topic.
Resulting document is a mixture of topics
Слайд 8Main goal of the algorithm
To invert the generative process, inferring
the set of topics that were responsible for generating a
collection of documents.
