Extracting meaningful drug-related information chunks, such as adverse drug
events (ADE), is crucial for preventing morbidity and saving many lives. Most
ADE are reported via an unstructured conversation with the medical context.
Hence, applying a general entity recognition approach is not sufficient enough.
The key is how to integrate and align multiple crucial aspects to detect drug
event information, including drug event semantics, syntactic structures, and
medical domain terminology. In this paper, we propose a new multi-aspect
cross-integration framework for drug entity/event detection by capturing and
aligning different context/language/knowledge properties from drug-related
documents. We first construct multi-aspect encoders to describe semantic,
syntactic, and medical document contextual information by conducting those slot
tagging tasks, main drug entity/event detection, part-of-speech tagging, and
general medical named entity recognition. Then, each encoder conducts cross
integration and alignment with other contextual information in three ways,
including the key-value cross, attention cross, and feedforward cross, so the
multi-encoders are integrated in depth. Then, we perform extensive experiments
on two widely used drug-related entity recognition downstream tasks, flat
entity detection and discontinuous event extraction. Our model significantly
outperforms all recent twelve state-of-the-art models. The implementation code
will be released at~https://github.com/adlnlp/mc-dre.

提出了一种用于药物实体 / 事件检测的新型多方面交叉整合框架，通过捕捉和对齐来自药物相关文档的不同上下文 / 语言 / 知识属性，包括药物事件语义、句法结构和医学领域术语。模型在广泛实验中表现优越，明显超过了十二种最新的先进模型。