Distantly supervised named entity recognition (DS-NER) aims to locate entity
mentions and classify their types with only knowledge bases or gazetteers and
unlabeled corpus. However, distant annotations are noisy and degrade the
performance of NER models. In this paper, we propose a noise-robust prototype
network named MProto for the DS-NER task. Different from previous
prototype-based NER methods, MProto represents each entity type with multiple
prototypes to characterize the intra-class variance among entity
representations. To optimize the classifier, each token should be assigned an
appropriate ground-truth prototype and we consider such token-prototype
assignment as an optimal transport (OT) problem. Furthermore, to mitigate the
noise from incomplete labeling, we propose a novel denoised optimal transport
(DOT) algorithm. Specifically, we utilize the assignment result between Other
class tokens and all prototypes to distinguish unlabeled entity tokens from
true negatives. Experiments on several DS-NER benchmarks demonstrate that our
MProto achieves state-of-the-art performance. The source code is now available
on Github.

Distantly supervised named entity recognition (DS-NER) with noise-robust prototype network MProto and denoised optimal transport (DOT) algorithm achieves state-of-the-art performance.

MProto: 多原型网络与去噪最优输运在远程监督命名实体识别中的应用

MProto: Multi-Prototype Network with Denoised Optimal Transport for  Distantly Supervised Named Entity Recognition

Distantly supervised named entity recognition (DS-NER) has been proposed to
exploit the automatically labeled training data instead of human annotations.
The distantly annotated datasets are often noisy and contain a considerable
number of false negatives. The recent approach uses a weighted sampling
approach to select a subset of negative samples for training. However, it
requires a good classifier to assign weights to the negative samples. In this
paper, we propose a simple and straightforward approach for selecting the top
negative samples that have high similarities with all the positive samples for
training. Our method achieves consistent performance improvements on four
distantly supervised NER datasets. Our analysis also shows that it is critical
to differentiate the true negatives from the false negatives.

本文提出了一种简单直接的方法，通过选择与所有正样本相似度高的负样本来训练，以提高四个远程监督 NER 数据集的性能表现，并且分析表明有必要区分真负样本和假负样本。

远距监督命名实体识别负例更好的采样

Better Sampling of Negatives for Distantly Supervised Named Entity  Recognition

Distantly supervised named entity recognition (DS-NER) efficiently reduces
labor costs but meanwhile intrinsically suffers from the label noise due to the
strong assumption of distant supervision. Typically, the wrongly labeled
instances comprise numbers of incomplete and inaccurate annotation noise, while
most prior denoising works are only concerned with one kind of noise and fail
to fully explore useful information in the whole training set. To address this
issue, we propose a robust learning paradigm named Self-Collaborative Denoising
Learning (SCDL), which jointly trains two teacher-student networks in a
mutually-beneficial manner to iteratively perform noisy label refinery. Each
network is designed to exploit reliable labels via self denoising, and two
networks communicate with each other to explore unreliable annotations by
collaborative denoising. Extensive experimental results on five real-world
datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising
methods.

本文提出一种名为 “自我协同去噪学习” 的鲁棒学习模式，利用两个教师 - 学生网络相互学习进行标签去噪，以应对远程监督命名实体识别中的标签噪声问题。该方法在五个真实世界数据集上的实验结果表明其优于当前 DS-NER 去噪方法。