SANTA：针对远程监督命名实体识别中不准确与不完整标注噪声的分离策略
SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition
AI总结：
本文提出SANTA框架，针对远程监督命名实体识别中的不准确和不完整标注噪声分别采用定制策略，有效缓解了实体歧义和决策边界偏移问题，并在五个公开数据集上达到最先进水平。
摘要：
远程监督命名实体识别有效缓解了监督设置中耗时且昂贵的标注负担。但无上下文匹配过程和知识库的有限覆盖分别引入了不准确和不完整的标注噪声。以往的研究要么仅考虑不完整标注噪声，要么用同一策略不加区分地处理两类噪声。本文认为，两类噪声的不同成因要求在模型架构中采用不同策略。因此，我们提出SANTA分别处理这两类噪声：(1) Memory-smoothed Focal Loss和Entity-aware KNN，以缓解不准确标注引起的实体歧义问题；(2) Boundary Mixup，以缓解不完整标注引起的决策边界偏移问题，并采用噪声容忍损失提升鲁棒性。得益于分离的定制策略，实验证实两类噪声均得到良好缓解。SANTA还在五个公开数据集上实现了新的最先进水平。
英文摘要：
Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting. But the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. Previous studies either considered only incomplete annotation noise or indiscriminately handle two types of noise with the same strategy. In this paper, we argue that the different causes of two types of noise bring up the requirement of different strategies in model architecture. Therefore, we propose the SANTA to handle these two types of noise separately with (1) Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate decision boundary shifting problem caused by incomplete annotation and a noise-tolerant loss to improve the robustness. Benefiting from our separate tailored strategies, we confirm in the experiment that the two types of noise are well mitigated. SANTA also achieves a new state-of-the-art on five public datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
