Statistical topic models for multi-label document classification

Timothy N. Rubin; America Chambers; Padhraic Smyth; Mark Steyvers

doi:10.1007/s10994-011-5272-5

Statistical topic models for multi-label document classification

Machine Learning ◽

10.1007/s10994-011-5272-5 ◽

2011 ◽

Vol 88 (1-2) ◽

pp. 157-208 ◽

Cited By ~ 133

Author(s):

Timothy N. Rubin ◽

America Chambers ◽

Padhraic Smyth ◽

Mark Steyvers

Keyword(s):

Topic Models ◽

Document Classification ◽

Statistical Topic Models

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Analyzing Entities and Topics in News Articles Using Statistical Topic Models

Intelligence and Security Informatics - Lecture Notes in Computer Science ◽

10.1007/11760146_9 ◽

2006 ◽

pp. 93-104 ◽

Cited By ~ 39

Author(s):

David Newman ◽

Chaitanya Chemudugunta ◽

Padhraic Smyth ◽

Mark Steyvers

Keyword(s):

Topic Models ◽

Statistical Topic Models

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A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency

Symmetry ◽

10.3390/sym11121486 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1486

Author(s):

Zhinan Gou ◽

Zheng Huo ◽

Yuanzhen Liu ◽

Yi Yang

Keyword(s):

Topic Modeling ◽

Topic Model ◽

State Of The Art ◽

Topic Models ◽

Document Classification ◽

Experimental Results ◽

Tag Recommendation ◽

Term Frequency ◽

Series Of Experiments ◽

Dirichlet Prior

Supervised topic modeling has been successfully applied in the fields of document classification and tag recommendation in recent years. However, most existing models neglect the fact that topic terms have the ability to distinguish topics. In this paper, we propose a term frequency-inverse topic frequency (TF-ITF) method for constructing a supervised topic model, in which the weight of each topic term indicates the ability to distinguish topics. We conduct a series of experiments with not only the symmetric Dirichlet prior parameters but also the asymmetric Dirichlet prior parameters. Experimental results demonstrate that the result of introducing TF-ITF into a supervised topic model outperforms several state-of-the-art supervised topic models.

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