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Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging

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We introduce DSDS: a cross-lingual neural part-of-speech tagger that learns from dis- parate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.
Original languageEnglish
Title of host publicationProceedings of the Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Publication date2018
Publication statusPublished - 2018

ID: 83324858