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.

翻译：我们提出一种简单方法，通过将类别标签和词性标注信息注入模型来改进生物医学命名实体识别。采用两种实现方式：第一种方法中，我们首先训练序列级分类器将句子划分为不同类别以获取句子级标签（类别标签），通过将标签改写为自然语言模板将序列分类器建模为蕴含问题，从而提升分类器精度，并将这些标签信息注入NER模型。本文展示了将这些标签和词性属性有效表示并注入NER模型的实用方法。第二种方法中，我们联合学习类别标签和NER标签，同时将词性标签注入模型以增强句法上下文。在三个基准数据集上的实验表明，融合句法上下文的类别标签信息具有显著效用，其性能优于基于BERT的基线模型。