Global models of document structure using latent permutations
We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selec...
| Main Authors: | , , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computational Linguistics,
2010-10-14T12:43:57Z.
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| Subjects: | |
| Online Access: | Get fulltext |