Named Entity Recognition (NER) is a well-studied problem in NLP. However,
there is much less focus on studying NER datasets, compared to developing new
NER models. In this paper, we employed three simple techniques to detect
annotation errors in the OntoNotes 5.0 corpus for English NER, which is the
largest available NER corpus for English. Our techniques corrected ~10% of the
sentences in train/dev/test data. In terms of entity mentions, we corrected the
span and/or type of ~8% of mentions in the dataset, while
adding/deleting/splitting/merging a few more. These are large numbers of
changes, considering the size of OntoNotes. We used three NER libraries to
train, evaluate and compare the models trained with the original and the
re-annotated datasets, which showed an average improvement of 1.23% in overall
F-scores, with large (>10%) improvements for some of the entity types. While
our annotation error detection methods are not exhaustive and there is some
manual annotation effort involved, they are largely language agnostic and can
be employed with other NER datasets, and other sequence labelling tasks.

该研究通过使用三种简单技术在 OntoNotes 5.0 语料库中检测注释错误，对英文命名实体识别（Named Entity Recognition，NER）的训练集、开发集和测试集进行了大量修正，提高了 1.23% 的整体 F 分数，对某些实体类型的改进超过 10%。这些技术可应用于其他 NER 数据集和其他序列标记任务。

注释错误与 NER：OntoNotes 5.0 研究

Annotation Errors and NER: A Study with OntoNotes 5.0

Existing approaches for Chinese zero pronoun resolution overlook semantic
information. This is because zero pronouns have no descriptive information,
which results in difficulty in explicitly capturing their semantic similarities
with antecedents. Moreover, when dealing with candidate antecedents,
traditional systems simply take advantage of the local information of a single
candidate antecedent while failing to consider the underlying information
provided by the other candidates from a global perspective. To address these
weaknesses, we propose a novel zero pronoun-specific neural network, which is
capable of representing zero pronouns by utilizing the contextual information
at the semantic level. In addition, when dealing with candidate antecedents, a
two-level candidate encoder is employed to explicitly capture both the local
and global information of candidate antecedents. We conduct experiments on the
Chinese portion of the OntoNotes 5.0 corpus. Experimental results show that our
approach substantially outperforms the state-of-the-art method in various
experimental settings.

该研究提出了一种新的零代词特定神经网络，通过利用语义层面上的上下文信息来表示零代词，并使用两级候选人编码器来明确捕获候选人的本地和全局信息，实验结果表明，在各种实验设置中，我们的方法明显优于现有技术。