Classifying biomedical citations without labeled training examples

Publication Type

Conference Proceeding Article

Publication Date

12-2004

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.

Discipline

Artificial Intelligence and Robotics | Health Information Technology

Publication

Proceedings Fourth IEEE International Conference on Data Mining, ICDM 2004

First Page

455

Last Page

458

ISBN

9780769521428

Identifier

10.1109/ICDM.2004.10039

Publisher

IEEE

City or Country

Brighton, UK

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