@InProceedings{abualhaija-EtAl:2017:RANLP,
  author    = {Abualhaija, Sallam  and  Tahmasebi, Nina  and  Forin, Diane  and  Zimmermann, Karl-Heinz},
  title     = {Parameter Transfer across Domains for Word Sense Disambiguation},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {1--8},
  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.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_001}
}