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

Barbara Plank, Zeljko Agic

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review


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.
TitelProceedings of the Conference on Empirical Methods in Natural Language Processing
ForlagAssociation for Computational Linguistics
StatusUdgivet - 2018


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