Please use this identifier to cite or link to this item: https://open.ni.ac.rs/handle/123456789/3467
Title: K-best max-margin approaches for sequence labeling
Authors: Mančev, Dejan 
Todorović B.
Issue Date: 1-Jan-2015
Journal: Computer Science and Information Systems
Abstract: © 2015 ComSIS Consortium. All rights reserved. Structured learning algorithms usually require inference during the training procedure. Due to their exponential size of output space, the parameter update is performed only on a relatively small collection built from the “best” structures. The k-best MIRA is an example of an online algorithm which seeks optimal parameters by making updates on k structures with the highest score at a time. Following the idea of using k-best structures during the learning process, in this paper we introduce four new k-best extensions of max-margin structured algorithms. We discuss their properties and connection, and evaluate all algorithms on two sequence labeling problems, the shallow parsing and named entity recognition. The experiments show how the proposed algorithms are affected by the changes of k in terms of the F-measure and computational time, and that the proposed algorithms can improve results in comparison to the single best case. Moreover, the restriction to the single best case produces a comparison of the existing algorithms.
URI: https://open.ni.ac.rs/handle/123456789/3467
ISSN: 18200214
DOI: 10.2298/CSIS140713014M
Appears in Collections:Naučne i umetničke publikacije

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