How Long to Read Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

How Long Does it Take to Read Scalable and Efficient Probabilistic Topic Model Inference for Textual Data?

It takes the average reader and 53 minutes to read Scalable and Efficient Probabilistic Topic Model Inference for Textual Data by Måns Magnusson

Assuming a reading speed of 250 words per minute. Learn more

Description

Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model. Probabilistiska ämnesmodeller (topic models) är en mångsidig klass av modeller för att estimera ämnessammansättningar i större corpusar. Applikationer finns i ett flertal vetenskapsområden som teknik, naturvetenskap, samhällsvetenskap och humaniora. I denna avhandling föreslås nya effektiva och parallella Markov Chain Monte Carlo algoritmer för Bayesianska ämnesmodeller. De föreslagna metoderna skalar väl med storleken på corpuset och kan användas för flera olika ämnesmodeller och liknande modeller inom språkteknologi. De föreslagna metoderna är snabba, effektiva, skalbara och konvergerar till den sanna posteriorfördelningen. Dessutom föreslås en ämnesmodell för högdimensionell textklassificering, med tonvikt på tolkningsbar dokumentklassificering genom att använda en kraftigt regulariserande priorifördelningar. Slutligen utvecklas en ämnesmodell för att analyzera "agenda" och "framing" för ett förutbestämt ämne. Med denna metod analyserar vi invandringsdiskursen i Sveriges Riksdag över tid, genom att kombinera teori från statsvetenskap, kommunikationsvetenskap och probabilistiska ämnesmodeller.

How long is Scalable and Efficient Probabilistic Topic Model Inference for Textual Data?

Scalable and Efficient Probabilistic Topic Model Inference for Textual Data by Måns Magnusson is 53 pages long, and a total of 13,409 words.

This makes it 18% the length of the average book. It also has 16% more words than the average book.

How Long Does it Take to Read Scalable and Efficient Probabilistic Topic Model Inference for Textual Data Aloud?

The average oral reading speed is 183 words per minute. This means it takes 1 hour and 13 minutes to read Scalable and Efficient Probabilistic Topic Model Inference for Textual Data aloud.

What Reading Level is Scalable and Efficient Probabilistic Topic Model Inference for Textual Data?

Scalable and Efficient Probabilistic Topic Model Inference for Textual Data is suitable for students ages 8 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

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