Relaxation Labeling for Selecting and Exploiting Efficiently Non-local Dependencies in Sequence Labeling

Abstract

Abstract. We consider the problem of sequence labeling and propose a two steps method which combines the scores of local classifiers with a relaxation labeling technique. This framework can account for sparse dynamically changing dependencies, which allows us to efficiently discover relevant non-local dependencies and exploit them. This is in contrast to existing models which incorporate only local relationships between neighboring nodes. Experimental results show that the proposed method gives promising results.