Advanced End-to-End Relation Extraction with Deep Neural Networks

摘要 连接各种生物医学实体的关系构成了实现生物医学数据科学的关键资源 应用和知识发现。关系信息跨越了翻译科学的范围， 从生物学（例如，蛋白质-蛋白质相互作用）到翻译生物信息学（例如，基因-疾病关联）， 并最终用于临床护理（例如，药物-药物相互作用）。科学家报告了新发现的nat- 通过同行评审的文献和医生可以在临床笔记中交流他们的语言。更 最近，患者还在社交媒体上报告副作用和不良事件。随着人口的指数增长， 生物医学自然语言处理（BioNLP）方法的进步正在变得越来越突出， 生物医学关系抽取（BRE）。BRE目前的大多数工作都遵循管道方法， 命名实体识别（NER），实体规范化（EN）和关系分类（RC）作为子任务。他们 流水线组件中的错误导致更多的下游错误 - 从而导致整个BRE系统的较低性能。这种情况导致了不同的评价 BRE子桩单独进行。在本提案中，我们强烈主张严格进行端到端评估 其中关系将从原始文本产生。我们提出了新的深度神经网络架构， 以端到端的方式对BRE进行建模，并在单个 过去的我们还将我们的架构扩展到n元和跨句子设置，其中两个以上的实体可以 即使关系是在多个句子中表达的，也需要联系起来。我们还建议建立两个 新的黄金标准BRE数据集，一个是药物-疾病治疗关系数据集，另一个是同类数据集 用于联合药物治疗。我们的主要假设是，我们的端到端提取模型将产生苏佩- 与传统管道相比，性能更好。我们通过（1）来检验这个问题。基于内在评价 标准性能指标与几个黄金标准数据集和（2）。面向外部应用的 在信息检索、问题回答和知识中，对使用案例提取的关系进行评估 基地完成。作为该项目一部分开发的所有软件和数据将供公众使用， 我们希望这将促进BRE系统严格的端到端基准测试。

ABSTRACT Relations linking various biomedical entities constitute a crucial resource that enables biomedical data science applications and knowledge discovery. Relational information spans the translational science spectrum going from biology (e.g., protein–protein interactions) to translational bioinformatics (e.g., gene–disease associations), and eventually to clinical care (e.g., drug–drug interactions). Scientists report newly discovered relations in nat- ural language through peer-reviewed literature and physicians may communicate them in clinical notes. More recently, patients are also reporting side-effects and adverse events on social media. With exponential growth in textual data, advances in biomedical natural language processing (BioNLP) methods are gaining prominence for biomedical relation extraction (BRE) from text. Most current efforts in BRE follow a pipeline approach containing named entity recognition (NER), entity normalization (EN), and relation classification (RC) as subtasks. They typically suffer from error snowballing — errors in a component of the pipeline leading to more downstream errors — resulting in lower performance of the overall BRE system. This situation has lead to evaluation of different BRE substaks conducted in isolation. In this proposal we make a strong case for strictly end-to-end evaluations where relations are to be produced from raw text. We propose novel deep neural network architectures that model BRE in an end-to-end fashion and directly identify relations and corresponding entity spans in a single pass. We also extend our architectures to n-ary and cross-sentence settings where more than two entities may need to be linked even as the relation is expressed across multiple sentences. We also propose to create two new gold standard BRE datasets, one for drug–disease treatment relations and another first of a kind dataset for combination drug therapies. Our main hypothesis is that our end-to-end extraction models will yield supe- rior performance when compared with traditional pipelines. We test this through (1). intrinsic evaluations based on standard performance measures with several gold standard datasets and (2). extrinsic application oriented assessments of relations extracted with use-cases in information retrieval, question answering, and knowledge base completion. All software and data developed as part of this project will be made available for public use and we hope this will foster rigorous end-to-end benchmarking of BRE systems.