Biomedical named entity recognition is one of the core tasks in biomedical
natural language processing (BioNLP). To tackle this task, numerous
supervised/distantly supervised approaches have been proposed. Despite their
remarkable success, these approaches inescapably demand laborious human effort.
To alleviate the need of human effort, dictionary-based approaches have been
proposed to extract named entities simply based on a given dictionary. However,
one downside of existing dictionary-based approaches is that they are
challenged to identify concept synonyms that are not listed in the given
dictionary, which we refer as the synonym generalization problem. In this
study, we propose a novel Synonym Generalization (SynGen) framework that
recognizes the biomedical concepts contained in the input text using span-based
predictions. In particular, SynGen introduces two regularization terms, namely,
(1) a synonym distance regularizer; and (2) a noise perturbation regularizer,
to minimize the synonym generalization error. To demonstrate the effectiveness
of our approach, we provide a theoretical analysis of the bound of synonym
generalization error. We extensively evaluate our approach on a wide range of
benchmarks and the results verify that SynGen outperforms previous
dictionary-based models by notable margins. Lastly, we provide a detailed
analysis to further reveal the merits and inner-workings of our approach.

本研究提出了一种新的同义词泛化（SynGen）框架，使用基于跨度的预测识别输入文本中包含的生物医学概念。 通过引入同义词距离正则化器和噪声扰动正则化器，该框架能够降低同义词泛化误差。本研究在多个基准测试中广泛评估了我们的方法，结果表明，SynGen 表现出比以前的基于字典的模型更优异的性能。