NDLI: πLDA: document clustering with selective structural constraints

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πLDA: document clustering with selective structural constraints

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πLDA: document clustering with selective structural constraints

Content Provider	ACM Digital Library
Author	Wang, Hanqi Wu, Fei Zhuang, Yueting Shao, Jian Tang, Siliang Chen, Ming
Abstract	Segments, such as sentence boundaries in texts or annotated regions in images, can be considered as useful structural constraints (i.e., priors) for unsupervised topic modeling. However, some segment units (e.g., words in texts or visual words in images) inside a given segment may be irrelevant to the topic of this segment due to their characteristics. This paper proposes a model called πLDA, which introduces a latent variable π into LDA, a traditional topic model, to capture the characteristic of each segment unit. That is to say, the πLDA model is conducted to determine whether a segment unit is assigned (or selected) to the topic embedded in its corresponding segment. Compared with other approaches that assume all the segment units in one segment to share a common topic, our proposed πLDA has the selective ability to discover the discriminative segment units (e.g., informative words or visual words). Experimental results and interpretations of them are presented for demonstrating the promising performance of our method.
Starting Page	753
Ending Page	756
Page Count	4
File Format	PDF
ISBN	9781450324045
DOI	10.1145/2502081.2502196
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Selective structural constraints Topic modeling Latent dirichlet allocation
Content Type	Text
Resource Type	Article

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