EXCEEDS: Extracting Complex Events as Connecting the Dots to Graphs in Scientific Domain
CoRR(2024)
Abstract
It is crucial to utilize events to understand a specific domain. There are
lots of research on event extraction in many domains such as news, finance and
biology domain. However, scientific domain still lacks event extraction
research, including comprehensive datasets and corresponding methods. Compared
to other domains, scientific domain presents two characteristics: denser
nuggets and more complex events. To solve the above problem, considering these
two characteristics, we first construct SciEvents, a large-scale multi-event
document-level dataset with a schema tailored for scientific domain. It has
2,508 documents and 24,381 events under refined annotation and quality control.
Then, we propose EXCEEDS, a novel end-to-end scientific event extraction
framework by storing dense nuggets in a grid matrix and simplifying complex
event extraction into a dot construction and connection task. Experimental
results demonstrate state-of-the-art performances of EXCEEDS on SciEvents.
Additionally, we release SciEvents and EXCEEDS on GitHub.
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