Frank, E., Chui, C. & Witten, I.H. (2000). Text categorization using compression models. (Working paper 00/02). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Permanent Research Commons link: https://hdl.handle.net/10289/1019
Text categorization, or the assignment of natural language texts to predefined categories based on their content, is of growing importance as the volume of information available on the internet continues to overwhelm us. The use of predefined categories implies a “supervised learning” approach to categorization, where already-classified articles which effectively define the categories are used as “training data” to build a model that can be used for classifying new articles that comprise the “test data”. This contrasts with “unsupervised” learning, where there is no training data and clusters of like documents are sought amongst the test articles. With supervised learning, meaningful labels (such as keyphrases) are attached to the training documents, and appropriate labels can be assigned automatically to test documents depending on which category they fall into.
University of Waikato, Department of Computer Science
- 2000 Working Papers