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

Learning target side syntactic structure has been shown to improve Neural
Machine Translation (NMT). However, incorporating syntax through latent
variables introduces additional complexity in inference, as the models need to
marginalize over the latent syntactic structures. To avoid this, models often
resort to greedy search which only allows them to explore a limited portion of
the latent space. In this work, we introduce a new latent variable model,
LaSyn, that captures the co-dependence between syntax and semantics, while
allowing for effective and efficient inference over the latent space. LaSyn
decouples direct dependence between successive latent variables, which allows
its decoder to exhaustively search through the latent syntactic choices, while
keeping decoding speed proportional to the size of the latent variable
vocabulary. We implement LaSyn by modifying a transformer-based NMT system and
design a neural expectation maximization algorithm that we regularize with
part-of-speech information as the latent sequences. Evaluations on four
different MT tasks show that incorporating target side syntax with LaSyn
improves both translation quality, and also provides an opportunity to improve
diversity.

本文介绍一种新的基于 latent variable 的神经机器翻译模型 LaSyn，通过修改基于 transformer 的 NMT 系统并使用基于词性信息的神经期望最大化算法作为 latent sequences 的正则化方法，实现了对目标语言句子结构的有效和高效推理，从而提高了翻译的质量和多样性。