Biomedical entity linking and event extraction are two crucial tasks to
support text understanding and retrieval in the biomedical domain. These two
tasks intrinsically benefit each other: entity linking disambiguates the
biomedical concepts by referring to external knowledge bases and the domain
knowledge further provides additional clues to understand and extract the
biological processes, while event extraction identifies a key trigger and
entities involved to describe each biological process which also captures the
structural context to better disambiguate the biomedical entities. However,
previous research typically solves these two tasks separately or in a pipeline,
leading to error propagation. What's more, it's even more challenging to solve
these two tasks together as there is no existing dataset that contains
annotations for both tasks. To solve these challenges, we propose joint
biomedical entity linking and event extraction by regarding the event
structures and entity references in knowledge bases as latent variables and
updating the two task-specific models in a hard Expectation-Maximization (EM)
fashion: (1) predicting the missing variables for each partially annotated
dataset based on the current two task-specific models, and (2) updating the
parameters of each model on the corresponding pseudo completed dataset.
Experimental results on two benchmark datasets: Genia 2011 for event extraction
and BC4GO for entity linking, show that our joint framework significantly
improves the model for each individual task and outperforms the strong
baselines for both tasks. We will make the code and model checkpoints publicly
available once the paper is accepted.

本文提出了一种基于隐含变量的硬 EM 方法，联合进行生物医学实体链接和事件抽取，并通过 Genia 2011 和 BC4GO 两个基准数据集的实验结果展示了该方法在两个任务上均优于强基线模型。