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Extracting meaningful drug-related information chunks, such as adverse drugevents (ADE), is crucial for preventing morbidity and saving many lives. MostADEs are reported via an unstructured conversation with the medical context, soapplying a general entity recognition approach is not sufficient enough. Inthis paper, we propose a new multi-aspect cross-integration framework for drugentity/event detection by capturing and aligning differentcontext/language/knowledge properties from drug-related documents. We firstconstruct multi-aspect encoders to describe semantic, syntactic, and medicaldocument contextual information by conducting those slot tagging tasks, maindrug entity/event detection, part-of-speech tagging, and general medical namedentity recognition. Then, each encoder conducts cross-integration with othercontextual information in three ways: the key-value cross, attention cross, andfeedforward cross, so the multi-encoders are integrated in depth. Our modeloutperforms all SOTA on two widely used tasks, flat entity detection anddiscontinuous event extraction.

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