In this work, we explore the way to perform named entity recognition (NER)
using only unlabeled data and named entity dictionaries. To this end, we
formulate the task as a positive-unlabeled (PU) learning problem and
accordingly propose a novel PU learning algorithm to perform the task. We prove
that the proposed algorithm can unbiasedly and consistently estimate the task
loss as if there is fully labeled data. A key feature of the proposed method is
that it does not require the dictionaries to label every entity within a
sentence, and it even does not require the dictionaries to label all of the
words constituting an entity. This greatly reduces the requirement on the
quality of the dictionaries and makes our method generalize well with quite
simple dictionaries. Empirical studies on four public NER datasets demonstrate
the effectiveness of our proposed method. We have published the source code at
https://github.com/v-mipeng/LexiconNER.

提出一种新的 PU 学习算法，用于使用未标记的数据和命名实体词典执行命名实体识别任务，该方法不需要词典标记每个句子中的所有实体，也不需要词典标记构成实体的所有单词。通过对四个公共 NER 数据集的实证研究，证明了该方法的有效性。