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

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Naoki Yoshinaga, Masaru Kitsuregawa
• Keywords: EMNLP 2009, Key Idea, Natural Language Processing, Primitive Feature Vectors, Target Nlp Task