1,214 research outputs found
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.Comment: EMNLP 201
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