Improving classification accuracy using automatically extracted training data
Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, Rakesh Agrawal, Panayiotis Tsaparas, John C. Shafer · 2009
Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parallel thread, recent work from the NLP community suggests that for tasks such as natural language disambiguation even a simple algorithm can outperform a sophisticated one, if it is provided with large quantities of high quality training data. In those applications, training data occurs naturally in text corpora, and high quality training data sets running into billions of words have been reportedly used.