Summary of Hierarchical Mixtures Of Unigram Models For Short Text Clustering: the Role Of Beta-liouville Priors, by Massimo Bilancia and Samuele Magro
Hierarchical mixtures of Unigram models for short text clustering: the role of Beta-Liouville priors
by Massimo Bilancia, Samuele Magro
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to unsupervised classification of short text data using a Multinomial mixture model. The traditional Dirichlet prior distribution is replaced with the Beta-Liouville distribution, allowing for a more flexible correlation structure. The authors examine the theoretical properties of the new prior and derive update equations for a CAVI-based variational algorithm to estimate model parameters. A stochastic variant of the algorithm is also proposed to improve scalability. The paper concludes with data examples demonstrating effective strategies for setting Beta-Liouville hyperparameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to classify short texts without knowing what they’re about. It does this by changing how we use a special kind of model called the Multinomial mixture model. Instead of using something called the Dirichlet prior, it uses the Beta-Liouville distribution. This lets us connect different pieces of text in more flexible ways. The authors show that their new method works and even make a faster version to handle lots of data. |
Keywords
» Artificial intelligence » Classification » Mixture model » Unsupervised
