NDLI: Topic-bridged PLSA for cross-domain text classification

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Topic-bridged PLSA for cross-domain text classification

Content Provider	ACM Digital Library
Author	Dai, Wenyuan Yu, Yong Yang, Qiang Xue, Gui-Rong
Abstract	In many Web applications, such as blog classification and new-sgroup classification, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data in a related but different domain. Traditional text classification ap-proaches are not able to cope well with learning across different domains. In this paper, we propose a novel cross-domain text classification algorithm which extends the traditional probabilistic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled data, which come from different but related domains, into a unified probabilistic model. We call this new model Topic-bridged PLSA, or TPLSA. By exploiting the common topics between two domains, we transfer knowledge across different domains through a topic-bridge to help the text classification in the target domain. A unique advantage of our method is its ability to maximally mine knowledge that can be transferred between domains, resulting in superior performance when compared to other state-of-the-art text classification approaches. Experimental eval-uation on different kinds of datasets shows that our proposed algorithm can improve the performance of cross-domain text classification significantly.
Starting Page	627
Ending Page	634
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390441
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Topic-bridged plsa Cross-domain Text classification
Content Type	Text
Resource Type	Article

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