Classifying biomedical citations without labeled training examples
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 knowled...
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sg-smu-ink.sis_research-40052016-02-05T06:30:05Z Classifying biomedical citations without labeled training examples Li X., Joshi R., Ramachandaran S., Tze-Yun LEONG, 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. 2004-12-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/3005 info:doi/10.1109/ICDM.2004.10039 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Health Information Technology |
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Artificial Intelligence and Robotics Health Information Technology Li X., Joshi R., Ramachandaran S., Tze-Yun LEONG, Classifying biomedical citations without labeled training examples |
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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. |
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Li X., Joshi R., Ramachandaran S., Tze-Yun LEONG, |
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Li X., Joshi R., Ramachandaran S., Tze-Yun LEONG, |
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Li X., |
title |
Classifying biomedical citations without labeled training examples |
title_short |
Classifying biomedical citations without labeled training examples |
title_full |
Classifying biomedical citations without labeled training examples |
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Classifying biomedical citations without labeled training examples |
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Classifying biomedical citations without labeled training examples |
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classifying biomedical citations without labeled training examples |
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Institutional Knowledge at Singapore Management University |
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2004 |
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https://ink.library.smu.edu.sg/sis_research/3005 |
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