Named Entity Recognition (NER) and Relation Extraction (RE) are essential
tools in distilling knowledge from biomedical literature. This paper presents
our findings from participating in BioNLP Shared Tasks 2019. We addressed Named
Entity Recognition including nested entities extraction, Entity Normalization
and Relation Extraction. Our proposed approach of Named Entities can be
generalized to different languages and we have shown it's effectiveness for
English and Spanish text. We investigated linguistic features, hybrid loss
including ranking and Conditional Random Fields (CRF), multi-task objective and
token-level ensembling strategy to improve NER. We employed dictionary based
fuzzy and semantic search to perform Entity Normalization. Finally, our RE
system employed Support Vector Machine (SVM) with linguistic features.
Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with
standard error rate (SER) of 0.7159 and showed competitive performance on
PharmaCo NER task with F1-score of 0.8662. Our RE system ranked first in the
SeeDev-binary Relation Extraction Task with F1-score of 0.3738.

本篇论文介绍了我们参加 BioNLP Shared Tasks 2019 后在 Named Entity Recognition 和 Relation Extraction 方面的方法，我们的方法可以概括应用于不同语言的文本，包括英语和西班牙语，其中我们使用了语言特征，混合损失，多任务目标和令牌级合成策略以提高 Named Entity Recognition 的性能，使用基于字典的模糊和语义搜索来执行实体标准化， 最后，我们的 Relation Extraction 系统使用带语言特征的 Support Vector Machine，并在相关任务上表现出色。