A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

Alexander Erdmann, Salam Khalifa, Mai Oudah, Nizar Habash, Houda Bouamor

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input. Our technique involves creating a small grammar of closed-class affixes which can be written in a few hours. The grammar over generates analyses for word forms attested in a raw corpus which are disambiguated based on features of the linguistic base proposed for each form. Extending the grammar to cover orthographic, morpho-syntactic or lexical variation is simple, making it an ideal solution for challenging corpora with noisy, dialect-inconsistent, or otherwise non-standard content. In two evaluations, we consistently outperform competitive unsupervised baselines and approach the performance of state-of-the-art supervised models trained on large amounts of data, providing evidence for the value of linguistic input during preprocessing.
Original languageUndefined
Title of host publicationProceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology
Place of PublicationFlorence, Italy
PublisherAssociation for Computational Linguistics (ACL)
Number of pages12
StatePublished - Aug 1 2019

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