Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields
نویسندگان
چکیده
Low-resource named entity recognition is still an open problem in NLP. Most stateof-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world’s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a loglinear CRF baseline.
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تاریخ انتشار 2017