LSI-based semantic characterisation for automated text categorisation - Tan, Ping Ping (Faculty of Computer Science and Information Technology, 2009)
As knowledge acquisition remains a bottleneck, incorporating human judgement within intelligent systems is still a challenge. Supervised learning methods have shown to be able to assist humans in automated text categorization (ATC). However, the performance of such systems is largely dependent on the characteristics of the datasets. Without the understanding of why a classifier works well for certain datasets, it is difficult to generalise its application across domains. Furthermore, most training sets used in supervised ATC have category labels provided by human experts. Expert knowledge used in the task of categorization is often not captured via the mere process of manipulating category labels. This has resulted in lose of intended meanings while performing supervised ATC. Besides that, large text datasets often contain a greater deal of noise.