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|Title:||Classifying biomedical citations without labeled training examples||Authors:||Li, X.
|Issue Date:||2004||Citation:||Li, X.,Joshi, R.,Ramachandaran, S.,Leong, T.-Y. (2004). Classifying biomedical citations without labeled training examples. Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 : 455-458. ScholarBank@NUS Repository.||Abstract:||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.||Source Title:||Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004||URI:||http://scholarbank.nus.edu.sg/handle/10635/40540||ISBN:||0769521428|
|Appears in Collections:||Staff Publications|
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