Integrating Background Knowledge Into Text Classification

We present a description of three different algorithms that use background knowledge to improve text classifiers. One uses the background knowledge as an index into the set of training examples. The second method uses background knowledge to reexpress the training examples. The last method treats pieces of background knowledge as unlabeled examples, and actually classifies them. The choice of background knowledge affects each method's performance and we discuss which type of background knowledge is most useful for each specific method. 1 Using Background Knowledge Supervised learning algorithms rely on a corpus of labeled training examples to produce accurate automatic text classifiers. An insufficient number of training examples often results in learned models that are suboptimal when classifying previously unseen examples. Numerous different approaches have been taken to compensate for the lack of training examples. These include the use of unlabeled examples [Bennet and Demiri...

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Sarah Zelikovitz, Haym Hirsh
• Keywords: Background Knowledge, IJCAI 2003, IJCAI 2007, Training Examples, Unlabeled Examples