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Open-categorical text classification based on multi-LDA models
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DOI：10.1007/s00500-014-1374-x.png)
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Abstract
En 中文
We present a new and realistic problem, open-categorical text classification, which requires us to classify documents without the categorization system known beforehand. To solve this problem, we propose a novel approach to construct the categorization system and classify documents based on multi-latent Dirichlet allocation (LDA) models. We cluster topics and extract topical keywords to help category annotation. Subsequently, the LDA models are applied to predict the categories of documents comprehensively. Our result, amacro-averaged F1 measure of 84.02%, outperforms the state-of-the-art supervised and semi-supervised text classification methods.
Keywords:
Topic model
Text classification
Categorization system construction
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