We present a simple approach to improve biomedical named entity recognition
(NER) by injecting categorical labels and Part-of-speech (POS) information into
the model. We use two approaches, in the first approach, we first train a
sequence-level classifier to classify the sentences into categories to obtain
the sentence-level tags (categorical labels). The sequence classifier is
modeled as an entailment problem by modifying the labels as a natural language
template. This helps to improve the accuracy of the classifier. Further, this
label information is injected into the NER model. In this paper, we demonstrate
effective ways to represent and inject these labels and POS attributes into the
NER model. In the second approach, we jointly learn the categorical labels and
NER labels. Here we also inject the POS tags into the model to increase the
syntactic context of the model. Experiments on three benchmark datasets show
that incorporating categorical label information with syntactic context is
quite useful and outperforms baseline BERT-based models.

通过向模型注入分类标签和词性信息，我们提出了一种简单的方法来改进生物医学命名实体识别（NER）。我们使用两种方法：第一种方法是先训练一个序列级分类器将句子分类为不同的类别，获取句子级别的标签（分类标签）。将这些标签注入 NER 模型，将分类标签转化为自然语言模板形式来改进分类器的准确性。第二种方法是同时学习分类标签和 NER 标签，并将词性标签注入模型以增加句法上下文，实验证明将分类标签信息与句法上下文相结合是非常有用的，并且优于基于 BERT 的基准模型。

将分类标签和句法信息注入生物医学 NER

Injecting Categorical Labels and Syntactic Information into Biomedical  NER

Recent advances in large language models have prompted researchers to examine
their abilities across a variety of linguistic tasks, but little has been done
to investigate how models handle the interactions in meaning across words and
larger syntactic forms -- i.e. phenomena at the intersection of syntax and
semantics. We present the semantic notion of agentivity as a case study for
probing such interactions. We created a novel evaluation dataset by utilitizing
the unique linguistic properties of a subset of optionally transitive English
verbs. This dataset was used to prompt varying sizes of three model classes to
see if they are sensitive to agentivity at the lexical level, and if they can
appropriately employ these word-level priors given a specific syntactic
context. Overall, GPT-3 text-davinci-003 performs extremely well across all
experiments, outperforming all other models tested by far. In fact, the results
are even better correlated with human judgements than both syntactic and
semantic corpus statistics. This suggests that LMs may potentially serve as
more useful tools for linguistic annotation, theory testing, and discovery than
select corpora for certain tasks.

以 semantic notion of agentivity 为例，研究了 large language models 在句法和语义交互方面的表现；作者创建了一个新颖的评估数据集，发现 GPT-3 的表现非常好，甚至比语法和语义语料库的统计结果还要好，这表明大语言模型可能对语言注释、理论测试和发现比某些任务的语料库更有用。