Does SVM really scale up to large bag of words feature spaces? Motivation

This paper shows how semantic attributes can be used to improve object classification. The semantic attributes used fall into five groups: scene (e.g. ‘road’), colour (e.g. ‘green’), part (e.g. ‘face’), shape (e.g. ‘box’), and material (e.g. ‘wood’). We train a set of classifiers for individual semantic attributes, and use them to make predictions on new images (Figure 1). We can then use the s...

Processing unknown words is disproportionately important because of their high information content. It is crucial in domains with specialist vocabularies where relevant training material is scarce, for example: biological text. Unknown word processing often begins with Part of Speech (POS) tagging, where accuracy is typically 10% worse than on known words. We demonstrate that features extracted...