Classifying biomedical citations without labeled training examples
Conference Proceeding Article
In this paper we introduce a novel technique for classifying text citations without labeled training examples. We first utilize the search results of a general search engine as original training data. We then proposed a mutually reinforcing learning algorithm (MRL) to mine the classification knowledge and to "clean" the training data. With the help of a set of established domain-specific ontological terms or keywords, the MRL mining step derives the relevant classification knowledge. The MRL cleaning step then builds a Naive Bayes classifier based on the mined classification knowledge and tries to clean the training set. The MRL algorithm is iteratively applied until a clean training set is obtained. We show the effectiveness of the proposed technique in the classification of biomedical citations from a large medical literature database. © 2004 IEEE.
Artificial Intelligence and Robotics | Health Information Technology
Data Management and Analytics
Proceedings Fourth IEEE International Conference on Data Mining, ICDM 2004
City or Country
Li X., Joshi R., Ramachandaran S., and Tze-Yun LEONG.
Classifying biomedical citations without labeled training examples. (2004). Proceedings Fourth IEEE International Conference on Data Mining, ICDM 2004. 455-458. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3005
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