arXiv:2406.14075v3 Announce Type: replace Abstract: It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event extraction in scientific domain is still insufficiently supported by comprehensive datasets and tailored methods. Compared with other domains, scientific domain has two characteristics: (1) denser nuggets and events, and (2) more complex information forms. To solve the above problem, considering these two characteristics, we first construct SciEvents, a large-scale m
The continuous drive for more sophisticated AI applications necessitates improved data extraction and understanding from complex sources like scientific literature, making this research timely.
This development addresses a critical bottleneck in leveraging scientific knowledge for AI systems, potentially accelerating research and development across various scientific domains.
The availability of better datasets and methods for event extraction in scientific literature will enable more accurate and comprehensive AI analysis of research breakthroughs and trends.
- · AI researchers
- · Scientific research institutions
- · Bioinformatics
- · Drug discovery
- · Manual data extraction processes
Improved event extraction from scientific texts leads to more efficient knowledge graph construction and domain-specific AI model training.
Accelerated scientific discovery and innovation across fields like materials science, medicine, and climate research due to enhanced AI capabilities.
The democratization of access to complex scientific insights through AI tools, impacting patenting, startup formation, and industrial R&D cycles.
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Read at arXiv cs.CL