Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging
IT University of Copenhagen（哥本哈根信息技术大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
