@InProceedings{eskander-rambow-muresan:2018:SIGMORPHON,
  author    = {Eskander, Ramy  and  Rambow, Owen  and  Muresan, Smaranda},
  title     = {Automatically Tailoring Unsupervised Morphological Segmentation to the Language},
  booktitle = {Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {78--83},
  abstract  = {Morphological segmentation is beneficial for several natural language processing tasks dealing with large vocabularies. Unsupervised methods for morphological segmentation are essential for handling a diverse set of languages, including low-resource languages. Eskander et al. (2016) introduced a Language Independent Morphological Segmenter (LIMS) using Adaptor Grammars (AG) based on the best-on-average performing AG configuration. However, while LIMS worked best on average and outperforms other state-of-the-art unsupervised morphological segmentation approaches, it did not provide the optimal AG configuration for five out of the six languages. We propose two language-independent classifiers that enable the selection of the optimal or nearly-optimal configuration for the morphological segmentation of unseen languages.},
  url       = {http://www.aclweb.org/anthology/W18-5808}
}