Chinese NER is a difficult undertaking due to the ambiguity of Chinese
characters and the absence of word boundaries. Previous work on Chinese NER
focus on lexicon-based methods to introduce boundary information and reduce
out-of-vocabulary (OOV) cases during prediction. However, it is expensive to
obtain and dynamically maintain high-quality lexicons in specific domains,
which motivates us to utilize more general knowledge resources, e.g., search
engines. In this paper, we propose TURNER: The Uncertainty-based Retrieval
framework for Chinese NER. The idea behind TURNER is to imitate human behavior:
we frequently retrieve auxiliary knowledge as assistance when encountering an
unknown or uncertain entity. To improve the efficiency and effectiveness of
retrieval, we first propose two types of uncertainty sampling methods for
selecting the most ambiguous entity-level uncertain components of the input
text. Then, the Knowledge Fusion Model re-predict the uncertain samples by
combining retrieved knowledge. Experiments on four benchmark datasets
demonstrate TURNER's effectiveness. TURNER outperforms existing lexicon-based
approaches and achieves the new SOTA.

本文提出了一个基于不确定性抽取辅助知识的中文命名实体识别框架 TURNER，该框架利用搜索引擎等通用知识资源代替特定领域的高质量词典，通过不确定性抽样方法选择难以判断的实体，再由知识融合模型对这些实体进行重新预测。实验表明，TURNER 在四个基准数据集上的表现优于现有的基于词典的方法，取得了新的最优成绩。

TURNER：面向中文命名实体识别的基于不确定性的检索框架

TURNER: The Uncertainty-based Retrieval Framework for Chinese NER

Open Domain Question Answering requires systems to retrieve external
knowledge and perform multi-hop reasoning by composing knowledge spread over
multiple sentences. In the recently introduced open domain question answering
challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts
and compose facts to correctly answer questions. In our work, we learn a
semantic knowledge ranking model to re-rank knowledge retrieved through Lucene
based information retrieval systems. We further propose a "knowledge fusion
model" which leverages knowledge in BERT-based language models with externally
retrieved knowledge and improves the knowledge understanding of the BERT-based
language models. On both OpenBookQA and QASC datasets, the knowledge fusion
model with semantically re-ranked knowledge outperforms previous attempts.

本文提出一种基于语义知识排名模型和知识融合模型的开放领域问答方法，该方法通过整合基于 Lucene 的信息检索系统检索的知识和 BERT 语言模型中的外部检索知识，以正确回答问题。研究表明，基于知识融合模型的方法优于之前的尝试。