2007
NeurIPS
NeurIPS 2007
Supervised Topic Models
Abstract
We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.
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Topic Pioneer
- Fine-Tuning
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
📈
Trend Setter
- Large Language Models
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Keyword Pioneer
- knowledge retrieval
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Hot Topic Early Bird
- large language model
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Core Methods > Representation Learning
Natural Language Processing > Generation > Language Modeling
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Text Representation
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Supervised Learning
Artificial Intelligence > Core AI > Large Language Models
Machine Learning > Learning Types > Fine-Tuning
Machine Learning > Core Methods > Topic Modeling
Artificial Intelligence > Bayesian & Probabilistic > Variational Inference
Natural Language Processing > Applications > Topic Modeling