engleski

Distant supervision from disparate sources for low-resource part-of-speech tagging

We introduce DSDS: a cross-lingual neural part- of-speech tagger that learns from disparate 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.

low-resource languages ; part-of-speech tagging ; neural networks ; distant supervision

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