The most common Named Entity Recognizers are usually sequence taggers trained
on fully annotated corpora, i.e. the class of all words for all entities is
known. Partially annotated corpora, i.e. some but not all entities of some
types are annotated, are too noisy for training sequence taggers since the same
entity may be annotated one time with its true type but not another time,
misleading the tagger. Therefore, we are comparing three training strategies
for partially annotated datasets and an approach to derive new datasets for new
classes of entities from Wikipedia without time-consuming manual data
annotation. In order to properly verify that our data acquisition and training
approaches are plausible, we manually annotated test datasets for two new
classes, namely food and drugs.

本研究比较了三种部分标注数据集的训练策略以及从维基百科派生新实体类数据集的方法，并针对两个新类别（食品和药物）手动注释测试数据集以验证数据获取和训练方法的可行性。

部分注释数据的命名实体识别

Named Entity Recognition for Partially Annotated Datasets

In this paper, we propose a new system called ASET that allows users to
perform structured explorations of text collections in an ad-hoc manner. The
main idea of ASET is to use a new two-phase approach that first extracts a
superset of information nuggets from the texts using existing extractors such
as named entity recognizers and then matches the extractions to a structured
table definition as requested by the user based on embeddings. In our
evaluation, we show that ASET is thus able to extract structured data from
real-world text collections in high quality without the need to design
extraction pipelines upfront.

本文提出了一种名为 ASET 的新系统，允许用户以特定方式对文本集合进行结构化探索，使用含有命名实体识别器的现有抽取器首先从文本中提取信息，并使用嵌入依据用户的要求将抽取匹配到结构化表定义中，评估结果显示，ASET 能够高质量地从真实文本集合中抽取结构化数据，而无需事先设计抽取流程。

ASET: 文本集合的即席结构化探索 [扩展摘要]

ASET: Ad-hoc Structured Exploration of Text Collections [Extended Abstract]

We study learning named entity recognizers in the presence of missing entity
annotations. We approach this setting as tagging with latent variables and
propose a novel loss, the Expected Entity Ratio, to learn models in the
presence of systematically missing tags. We show that our approach is both
theoretically sound and empirically useful. Experimentally, we find that it
meets or exceeds performance of strong and state-of-the-art baselines across a
variety of languages, annotation scenarios, and amounts of labeled data. In
particular, we find that it significantly outperforms the previous
state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by
+12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased
annotations, averaged across 7 datasets. We also show that, when combined with
our approach, a novel sparse annotation scheme outperforms exhaustive
annotation for modest annotation budgets.

研究利用潜在变量标注的缺失实体标注学习命名实体识别器的方法，提出了期望实体比率的新损失函数，并证明其在理论和实践上的有效性和优越性。