@InProceedings{sumalvico:2017:RANLP,
  author    = {Sumalvico, Maciej},
  title     = {Unsupervised Learning of Morphology with Graph Sampling},
  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     = {723--732},
  abstract  = {We introduce a language-independent, graph-based probabilistic model of
	morphology, which uses transformation rules operating on whole words instead of
	the traditional morphological segmentation. The morphological analysis of a set
	of words is expressed through a graph having words as vertices and structural
	relationships between words as edges. We define a probability distribution over
	such graphs and develop a sampler based on the Metropolis-Hastings algorithm. 
	The sampling is applied in order to determine the strength of morphological
	relationships between words, filter out accidental similarities and reduce the
	set of rules necessary to explain the data. The model is evaluated on the task
	of finding pairs of morphologically similar words, as well as generating new
	words. The results are compared to a state-of-the-art segmentation-based
	approach.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_093}
}