Please use this identifier to cite or link to this item:
|Title:||Classifying biomedical citations without labeled training examples|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 24, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.