MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction

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~\url{https://github.com/adlnlp/mc-dre}.