@inproceedings{eskander-etal-2018-automatically,
    title = "Automatically Tailoring Unsupervised Morphological Segmentation to the Language",
    author = "Eskander, Ramy  and
      Rambow, Owen  and
      Muresan, Smaranda",
    editor = "Kuebler, Sandra  and
      Nicolai, Garrett",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-5808/",
    doi = "10.18653/v1/W18-5808",
    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."
}