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Poster
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
Shuang Yang · Hongyuan Zha · Bao-Gang Hu

Mon Dec 07 07:00 PM -- 11:59 PM (PST) @
in Poster Session »

We propose Dirichlet-Bernoulli Alignment (DBA), a generative model for corpora in which each pattern (e.g., a document) contains a set of instances (e.g., paragraphs in the document) and belongs to multiple classes. By casting predefined classes as latent Dirichlet variables (i.e., instance level labels), and modeling the multi-label of each pattern as Bernoulli variables conditioned on the weighted empirical average of topic assignments, DBA automatically aligns the latent topics discovered from data to human-defined classes. DBA is useful for both pattern classification and instance disambiguation, which are tested on text classification and named entity disambiguation for web search queries respectively.

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Author Information

Shuang Yang (Facebook)
Hongyuan Zha (Georgia Tech)
Bao-Gang Hu (NLPR, CASIA, China)

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