Improving Topic Coherence with Regularized Topic Models
Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval. However, when dealing with small collections or noisy text (e.g. web search result snippets or blog posts), learned topics can be less coherent, less interpretable, and less useful. To overcome this, we...[Show more]
|Collections||ANU Research Publications|
|Source:||Advances in Neural Information Processing Systems 23|
|01_Newman_Improving_Topic_Coherence_with_2011.pdf||211.84 kB||Adobe PDF||Request a copy|
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