caa a22 4500 605468370 CHVBK 20210128100316.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1450-2 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1450-2 NestDE: generic parameters tuning for automatic story segmentation [Elektronische Daten] [Wei Feng, Xuefei Yin, Yifeng Zhang, Lei Xie] Parameters tuning is a crucial task in automatic story segmentation. For most previous story segmentation methods, however, the parameters were simply derived from empirical tuning, which may indeed harm the fairness of the evaluation, or even misguide the conclusion. In this paper, we present a general parameters tuning approach, namely nested differential evolution. As a practical general-purpose parameters tuner, our approach itself is parameters-robust and is generic enough to optimize the most usual types of parameters for the given corpus and evaluation criterion. Besides, our approach is able to cooperate with empirical tuning and jointly produce better parameters based on the prior knowledge of experienced users. Extensive experiments on synthetic challenging quadratic pseudo-Boolean optimization and real-world story segmentation tasks validate the superior performance of our approach over traditional empirical tuning and other generic optimizers, such as simulated annealing and classical differential evolution. Springer-Verlag Berlin Heidelberg, 2014 Generic parameters tuning nationallicence Nested differential evolution (NestDE) nationallicence Automatic story segmentation nationallicence Quadratic pseudo-Boolean optimization (QPBO) nationallicence Feng Wei Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China aut Yin Xuefei Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China aut Zhang Yifeng Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China aut Xie Lei School of Computer Science, Northwestern Polytechnical University, Xi'an, China aut Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 61-70 1432-7643 19:1<61 2015 19 500 https://doi.org/10.1007/s00500-014-1450-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1450-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Feng Wei Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China aut NATIONALLICENCE 700 1- Yin Xuefei Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China aut NATIONALLICENCE 700 1- Zhang Yifeng Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China aut NATIONALLICENCE 700 1- Xie Lei School of Computer Science, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 61-70 1432-7643 19:1<61 2015 19 500