NER model has achieved promising performance on standard NER benchmarks.
However, recent studies show that previous approaches may over-rely on entity
mention information, resulting in poor performance on out-of-vocabulary (OOV)
entity recognition. In this work, we propose MINER, a novel NER learning
framework, to remedy this issue from an information-theoretic perspective. The
proposed approach contains two mutual information-based training objectives: i)
generalizing information maximization, which enhances representation via deep
understanding of context and entity surface forms; ii) superfluous information
minimization, which discourages representation from rote memorizing entity
names or exploiting biased cues in data. Experiments on various settings and
datasets demonstrate that it achieves better performance in predicting OOV
entities.

本文提出了一个新的网络实体识别学习框架 MINER，采用了两个信息论学习目标，分别是广义信息最大化和多余信息最小化，以提高网络的学习能力和泛化性，从而提升模型对 Out-of-vocabulary 实体的识别准确率。实验结果表明，该模型能够显著提高模型的概括能力和结构学习能力，有效提高了模型对未知实体的识别准确率。