Learning a two-stage SVM/CRF sequence classifier

Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "cat", based on three images of handwritten letters and on general knowledge of English letter combinations, is a sequence classification task. This paper describes a new two-stage approach to learning a sequence classifier that is (i) highly accurate, (ii) scalable, and (iii) easy to use in data mining applications. The two-stage approach combines support vector machines (SVMs) and conditional random fields (CRFs). It is (i) highly accurate because it benefits from the maximummargin nature of SVMs and also from the ability of CRFs to model correlations between neighboring output tags. It is (ii) scalable because the input to each SVM is a small training set, and the input to the CRF has a small number of features, namely the SVM outputs. It is (iii) easy to use because it combines existing published softwa...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Guilherme Hoefel, Charles Elkan
• Keywords: CIKM 2008, Conditional Random Fields, Information Management, Sequence Classifiers, Two-stage Approach