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.
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Wisniewski, G., Gallinari, P. (2007). Relaxation Labeling for Selecting and Exploiting Efficiently Non-local Dependencies in Sequence Labeling. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds) Knowledge Discovery in Databases: PKDD 2007. PKDD 2007. Lecture Notes in Computer Science(), vol 4702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74976-9_30
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DOI: https://doi.org/10.1007/978-3-540-74976-9_30
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