A conditional random field-based model for joint sequence segmentation and classification

In this paper, we consider the problem of joint segmentation and classification of sequences in the framework of conditional random field (CRF) models. To effect this goal, we introduce a novel dual-functionality CRF model: on the first level, the proposed model conducts sequence segmentation, whereas, on the second level, the whole observed sequences are classified into one of the available learned classes. These two procedures are conducted in a joint, synergetic fashion, thus optimally exploiting the information contained in the used model training sequences. Model training is conducted by means of an efficient likelihood maximization algorithm, and inference is based on the familiar Viterbi algorithm. We evaluate the efficacy of our approach considering a real-world application, and we compare its performance to popular alternatives.

► A method for joint sequence segmentation and classification is proposed. ► The method is based on conditional random fields with two levels of predictable variables. ► Evaluation is conducted using real-world datasets.