Named Entity Recognition (NER) performance often degrades rapidly when
applied to target domains that differ from the texts observed during training.
When in-domain labelled data is available, transfer learning techniques can be
used to adapt existing NER models to the target domain. But what should one do
when there is no hand-labelled data for the target domain? This paper presents
a simple but powerful approach to learn NER models in the absence of labelled
data through weak supervision. The approach relies on a broad spectrum of
labelling functions to automatically annotate texts from the target domain.
These annotations are then merged together using a hidden Markov model which
captures the varying accuracies and confusions of the labelling functions. A
sequence labelling model can finally be trained on the basis of this unified
annotation. We evaluate the approach on two English datasets (CoNLL 2003 and
news articles from Reuters and Bloomberg) and demonstrate an improvement of
about 7 percentage points in entity-level $F_1$ scores compared to an
out-of-domain neural NER model.

文章提出了一种简单但有效的方法，通过弱监督机制在没有标注数据的情况下学习 NER 模型，该方法使用广泛的标注功能对目标域中的文本进行自动注释，然后通过隐马尔可夫模型将这些注释合并在一起，从而最终基于这个统一的注释训练一个序列标注模型。通过在两个英文数据集上的评估（CoNLL 2003 和来自路透社和彭博社的新闻文章），相比于域外神经 NER 模型，在实体级别的 F1 得分上取得了约 7 个百分点的提升。

无标记数据下的命名实体识别：一种弱监督方法

Named Entity Recognition without Labelled Data: A Weak Supervision  Approach

This paper describes an efficient approach to improve the accuracy of a named
entity recognition system for Vietnamese. The approach combines regular
expressions over tokens and a bidirectional inference method in a sequence
labelling model. The proposed method achieves an overall $F_1$ score of 89.66%
on a test set of an evaluation campaign, organized in late 2016 by the
Vietnamese Language and Speech Processing (VLSP) community.

该论文提出了一种有效的方法，通过在标记序列模型中应用标记规则和双向推理方法来提高越南语命名实体识别系统的准确性，该方法在 VLSP 社区于 2016 年底组织的一次评估活动中的测试集上取得了 89.66％的总体 F1 得分。