低资源命名实体识别与跨语言字符级神经条件随机场
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
AI总结：
针对低资源命名实体识别标注不足的问题，本文提出联合训练字符级神经CRF的迁移学习方案，通过跨语言字符表示迁移，使F1值较对数线性CRF基线最高提升9.8个百分点。
摘要：
低资源命名实体识别仍是自然语言处理（NLP）领域的开放问题。多数最先进的系统需要数万条标注句子才能获得高性能，但对世界上大多数语言而言，获取此类标注并不可行。本文提出一种迁移学习方案，训练字符级神经CRF（条件随机场），联合预测高资源语言与低资源语言的命名实体。学习多种相关语言的字符表示可实现跨语言迁移，相比对数线性CRF基线，F1值最高提升9.8个百分点。
英文摘要：
Low-resource named entity recognition is still an open problem in NLP. Most state-of-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.
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。
