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EMNLP
2009

Polynomial to Linear: Efficient Classification with Conjunctive Features

8 years 11 months ago
Polynomial to Linear: Efficient Classification with Conjunctive Features
This paper proposes a method that speeds up a classifier trained with many conjunctive features: combinations of (primitive) features. The key idea is to precompute as partial results the weights of primitive feature vectors that appear frequently in the target NLP task. A trie compactly stores the primitive feature vectors with their weights, and it enables the classifier to find for a given feature vector its longest prefix feature vector whose weight has already been computed. Experimental results for a Japanese dependency parsing task show that our method speeded up the SVM and LLM classifiers of the parsers, which achieved accuracy of
Naoki Yoshinaga, Masaru Kitsuregawa
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EMNLP
Authors Naoki Yoshinaga, Masaru Kitsuregawa
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