EXCEEDS: Extracting Complex Events as Connecting the Dots to Graphs in Scientific Domain
This addresses the problem of extracting complex events in scientific texts, which is incremental as it adapts methods to a new domain with denser data.
The authors tackled the lack of event extraction research in the scientific domain by constructing SciEvents, a dataset with 2,508 documents and 24,381 events, and proposing EXCEEDS, a framework that achieved state-of-the-art performance on this dataset.
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.