Name tagging in low-resource languages or domains suffers from inadequate
training data. Existing work heavily relies on additional information, while
leaving those noisy annotations unexplored that extensively exist on the web.
In this paper, we propose a novel neural model for name tagging solely based on
weakly labeled (WL) data, so that it can be applied in any low-resource
settings. To take the best advantage of all WL sentences, we split them into
high-quality and noisy portions for two modules, respectively: (1) a
classification module focusing on the large portion of noisy data can
efficiently and robustly pretrain the tag classifier by capturing textual
context semantics; and (2) a costly sequence labeling module focusing on
high-quality data utilizes Partial-CRFs with non-entity sampling to achieve
global optimum. Two modules are combined via shared parameters. Extensive
experiments involving five low-resource languages and fine-grained food domain
demonstrate our superior performance (6% and 7.8% F1 gains on average) as well
as efficiency.

该研究提出了一种基于弱标注数据的名字标注神经模型，通过使用 Partial-CRFs 等技术，有效地解决了低资源语言或领域中不足的培训数据问题，并在五种低资源语言和细粒度的食品领域上表现出显著的性能和效率改善。