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	title        = {Parameter Transfer across Domains for Word Sense Disambiguation},
	abstract     = {Word  sense  disambiguation  is  defined  as finding the corresponding sense for a target word in a given context,  which comprises  a  major  step  in  text  applications. Recently, it has been addressed as an optimization problem.  The idea behind is to find a sequence of senses that corresponds
to the words in a given context with a maximum semantic similarity.  Metaheuristics like simulated annealing and D-Bees provide approximate good-enough solutions, but are usually influenced by the starting parameters. In this paper, we study the parameter tuning for both algorithms within the  word  sense  disambiguation  problem. The experiments are conducted on different datasets to cover different disambiguation scenarios. We show that D-Bees is robust and less sensitive towards the initial parameters compared to simulated annealing,  hence,  it is sufficient to tune the parameters once and reuse them for different datasets, domains or languages.},
	booktitle    = {Proceedings of Recent Advances in Natural Language Processing Meet Deep Learning, Varna, Bulgaria 2–8 September 2017 / Edited by Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Ivelina  Nikolova, Irina Temnikova  },
	author       = {Abualhajia, Sallam and Tahmasebi, Nina and Forin, Diane  and Zimmermann, Karl-Heinz},
	year         = {2017},
	ISBN         = { 978-954-452-048-9},