Index of papers in Proc. ACL 2012 that mention
  • feature space
Wang, William Yang and Mayfield, Elijah and Naidu, Suresh and Dittmar, Jeremiah
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.
feature space is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Zhao, Qiuye and Marcus, Mitch
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 .
feature space is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: