Information Extraction (IE) from text refers to the task of extracting structured knowledge from unstructured text. The task typically consists of a series of sub-tasks such as Named Entity Recognition and Relation Extraction. Sourcing entity and relation type specific training data is a major bottleneck in the above sub-tasks.In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing to deal with zero-shot settings. We follow the recently proposed paradigm of coupling a Tranformer-based bi-encoder, Dense Passage Retrieval, with a Transformer-based reader model to extract relations from biomedical text. We assemble a biomedical slot filling dataset for both retrieval and reading comprehension and conduct a series of experiments demonstrating that our approach outperforms a number of simpler baselines. We also evaluate our approach end-to-end for standard as well as zero-shot settings. Our work provides a fresh perspective on how to solve biomedical IE tasks, in the absence of relevant training data. Our code, models and pretrained data are available at https://github.com/healx/biomed-slot-filling.

中文翻译：

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用于生物医学信息提取的槽填充

从文本中提取信息（IE）是指从非结构化文本中提取结构化知识的任务。该任务通常由命名实体识别和关系提取等一系列子任务组成。采购实体和关系类型特定的训练数据是上述子任务的主要瓶颈。处理零镜头设置。我们遵循最近提出的范式，将基于 Transformer 的双编码器、Dense Passage Retrieval 与基于 Transformer 的阅读器模型耦合，以从生物医学文本中提取关系。我们为检索和阅读理解组装了一个生物医学插槽填充数据集，并进行了一系列实验，证明我们的方法优于许多更简单的基线。我们还评估了我们的标准和零样本设置的端到端方法。我们的工作为如何在缺乏相关训练数据的情况下解决生物医学 IE 任务提供了新的视角。我们的代码、模型和预训练数据可在 https://github.com/healx/biomed-slot-filling 获得。