Prediction Experiments | In terms of the size of vocabulary W for both the SME and SVM learner, we select three values to represent dense, medium or sparse feature spaces : W1 2 29, W2 2 212, and the full vocabulary size of W3 2 213'8. |
Prediction Experiments | For example, with a medium density feature space of 212, SVM obtained an accuracy of 35.8%, but SME achieved an accuracy of 40.9%, which is a 14.2% relative improvement (p < 0.001) over SVM. |
Prediction Experiments | When the feature space becomes sparser, the SME obtains an increased relative improvement (10 < 0.001) of 16.1%, using full size of vocabulary. |
Abstract | The score of tag predictions are usually computed in a high-dimensional feature space . |
Abstract | Consider a character-based feature function gb(c, t, c) that maps a character-tag pair to a high-dimensional feature space , with respect to an input character sequence c. For a possible word over c of length l , w, = 0,0 . |
Abstract | In Section 3.4, we describe a way of mapping words to a character-based feature space . |