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KDD
2009
ACM

Improving classification accuracy using automatically extracted training data

9 years 4 months ago
Improving classification accuracy using automatically extracted training data
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. We explore how we can apply the lessons from the NLP community to KDD tasks. Specifically, we investigate how to identify data sources that can yield training data at low cost and study whether the quantity of the automatically extracted training data can compensate for its lower quality. We carry out this investigation for the specific task of inferring whether a search query has commercial intent. We mine too...
Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, R
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, Rakesh Agrawal, Panayiotis Tsaparas, John C. Shafer
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