We consider the problem of Named Entity Recognition (NER) on biomedical
scientific literature, and more specifically the genomic variants recognition
in this work. Significant success has been achieved for NER on canonical tasks
in recent years where large data sets are generally available. However, it
remains a challenging problem on many domain-specific areas, especially the
domains where only small gold annotations can be obtained. In addition, genomic
variant entities exhibit diverse linguistic heterogeneity, differing much from
those that have been characterized in existing canonical NER tasks. The
state-of-the-art machine learning approaches in such tasks heavily rely on
arduous feature engineering to characterize those unique patterns. In this
work, we present the first successful end-to-end deep learning approach to
bridge the gap between generic NER algorithms and low-resource applications
through genomic variants recognition. Our proposed model can result in
promising performance without any hand-crafted features or post-processing
rules. Our extensive experiments and results may shed light on other similar
low-resource NER applications.

本文针对生物医学科学文献中的命名实体识别问题，尤其是基因组变异识别问题，提出了一种先进的端到端深度学习算法，实现了通用命名实体识别算法与低资源应用之间的桥梁，不需要手工特征工程或后处理规则，可望在低资源命名实体识别应用领域取得有希望的性能。