2009
NIPS
NeurIPS 2009
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
Abstract
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.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
📈
Trend Setter
— Weakly Supervised Learning
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Keyword Pioneer
— named entity disambiguation
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Hot Topic Early Bird
— text classification
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Applications > Text Classification
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Classification
Machine Learning > Learning Types > Multi-Label Classification