Named Entity Recognition (NER) is a sequence classification Natural Language
Processing task where entities are identified in the text and classified into
predefined categories. It acts as a foundation for most information extraction
systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with
its own diverse lore. DnD entities are domain-specific and are thus
unrecognizable by even the state-of-the-art off-the-shelf NER systems as the
NER systems are trained on general data for pre-defined categories such as:
person (PERS), location (LOC), organization (ORG), and miscellaneous (MISC).
For meaningful extraction of information from fantasy text, the entities need
to be classified into domain-specific entity categories as well as the models
be fine-tuned on a domain-relevant corpus. This work uses available lore of
monsters in the D&D domain to fine-tune Trankit, which is a prolific NER
framework that uses a pre-trained model for NER. Upon this training, the system
acquires the ability to extract monster names from relevant domain documents
under a novel NER tag. This work compares the accuracy of the monster name
identification against; the zero-shot Trankit model and two FLAIR models. The
fine-tuned Trankit model achieves an 87.86% F1 score surpassing all the other
considered models.

该研究通过使用 D&D 领域的可用怪物相关知识对 Trankit 进行微调，训练系统在新的 NER 标签下从相关领域文档中提取怪物名称，并将其与零样本 Trankit 模型和两个 FLAIR 模型进行了准确性比较，结果显示经过微调的 Trankit 模型获得了 87.86% 的 F1 分数，超过其他考虑的模型。

针对奇幻领域的命名实体提取模型微调

Fine Tuning Named Entity Extraction Models for the Fantasy Domain

Traditional NER systems are typically trained to recognize coarse-grained
entities, and less attention is given to classifying entities into a hierarchy
of fine-grained lower-level subtypes. This article aims to advance Arabic NER
with fine-grained entities. We chose to extend Wojood (an open-source Nested
Arabic Named Entity Corpus) with subtypes. In particular, four main entity
types in Wojood, geopolitical entity (GPE), location (LOC), organization (ORG),
and facility (FAC), are extended with 31 subtypes. To do this, we first revised
Wojood's annotations of GPE, LOC, ORG, and FAC to be compatible with the LDC's
ACE guidelines, which yielded 5, 614 changes. Second, all mentions of GPE, LOC,
ORG, and FAC (~44K) in Wojood are manually annotated with the LDC's ACE
sub-types. We refer to this extended version of Wojood as WojoodF ine. To
evaluate our annotations, we measured the inter-annotator agreement (IAA) using
both Cohen's Kappa and F1 score, resulting in 0.9861 and 0.9889, respectively.
To compute the baselines of WojoodF ine, we fine-tune three pre-trained Arabic
BERT encoders in three settings: flat NER, nested NER and nested NER with
subtypes and achieved F1 score of 0.920, 0.866, and 0.885, respectively. Our
corpus and models are open-source and available at
this https URL

该研究论文旨在通过扩展 Wojood（一种开源的阿拉伯语嵌套命名实体语料库）的细分类型，提升阿拉伯语 NER 的准确性和细粒度，通过改变 Wojood 的注释，手动标注扩展 Wojood，并在三种设置下使用预训练的阿拉伯 BERT 编码器计算 WojoodFine 的基准，其 F1 分数分别为 0.920, 0.866, 和 0.885。

阿拉伯细粒度实体识别

Arabic Fine-Grained Entity Recognition

Named entity recognition (NER) systems have seen rapid progress in recent
years due to the development of deep neural networks. These systems are widely
used in various natural language processing applications, such as information
extraction, question answering, and sentiment analysis. However, the complexity
and intractability of deep neural networks can make NER systems unreliable in
certain circumstances, resulting in incorrect predictions. For example, NER
systems may misidentify female names as chemicals or fail to recognize the
names of minority groups, leading to user dissatisfaction. To tackle this
problem, we introduce TIN, a novel, widely applicable approach for
automatically testing and repairing various NER systems. The key idea for
automated testing is that the NER predictions of the same named entities under
similar contexts should be identical. The core idea for automated repairing is
that similar named entities should have the same NER prediction under the same
context. We use TIN to test two SOTA NER models and two commercial NER APIs,
i.e., Azure NER and AWS NER. We manually verify 784 of the suspicious issues
reported by TIN and find that 702 are erroneous issues, leading to high
precision (85.0%-93.4%) across four categories of NER errors: omission,
over-labeling, incorrect category, and range error. For automated repairing,
TIN achieves a high error reduction rate (26.8%-50.6%) over the four systems
under test, which successfully repairs 1,056 out of the 1,877 reported NER
errors.

利用 TIN 进行自动测试修复 NER 系统的错误。在测试中，TIN 发现了 784 个可疑问题，其中 702 个是错误问题，并成功修复了 1,056 个 NER 错误。

命名实体识别系统的自动测试和优化

Automated Testing and Improvement of Named Entity Recognition Systems

We present a novel approach for recognizing what we call targetable named
entities; that is, named entities in a targeted set (e.g, movies, books, TV
shows). Unlike many other NER systems that need to retrain their statistical
models as new entities arrive, our approach does not require such retraining,
which makes it more adaptable for types of entities that are frequently
updated. For this preliminary study, we focus on one entity type, movie title,
using data collected from Twitter. Our system is tested on two evaluation sets,
one including only entities corresponding to movies in our training set, and
the other excluding any of those entities. Our final model shows F1-scores of
76.19% and 78.70% on these evaluation sets, which gives strong evidence that
our approach is completely unbiased to any par- ticular set of entities found
during training.

我们提出了一种新方法用于识别目标化命名实体；我们的方法不需要重新训练统计模型，这使得它对于经常更新的实体类型更加适应。我们的系统使用从 Twitter 收集的数据，以电影标题为例进行测试。在两个评估集上进行测试，我们的最终模型显示 F1 得分为 76.19％和 78.70％，这表明我们的方法完全不偏向于任何训练过程中找到的特定实体集。