Knowledge Based Dimensionality Reduction for Technical Text Mining

In this paper we propose a novel technique for dimensionality reduction using freely available online knowledge bases. The complexity of our method is linearly proportional to the size of the full feature set, making it applicable efficiently to huge and complex datasets. We demonstrate this approach by investigating its effectiveness on patent data, the largest free technical text. We report empirical results on classification of the CLEF-IP 2010 dataset using bigram features supported by mentions in Wikipedia, Wiktionary, and GoogleBooks knowledge bases. We achieve a 13-fold reduction in number of bigrams features and a 1.7% increase in classification accuracy over the unigrams baseline. These results give concrete evidence that significant accuracy improvements and massive reduction in dimensionality could be achieved using our approach, hence help alleviating the tradeoff between task complexity and accuracy.