We present SciDMT, an enhanced and expanded corpus for scientific mention detection, offering a significant
advancement over existing related resources. SciDMT contains annotated scientific documents for datasets (D),
methods (M), and tasks (T). The corpus consists of two components: 1) the SciDMT main corpus, which includes 48
thousand scientific articles with over 1.8 million weakly annotated mention annotations in the format of in-text span,
and 2) an evaluation set, which comprises 100 scientific articles manually annotated for evaluation purposes. To the
best of our knowledge, SciDMT is the largest corpus for scientific entity mention detection. The corpus’s scale and
diversity are instrumental in developing and refining models for tasks such as indexing scientific papers, enhancing
information retrieval, and improving the accessibility of scientific knowledge. We demonstrate the corpus’s utility
through experiments with advanced deep learning architectures like SciBERT and GPT-3.5. Our findings establish
performance baselines and highlight unresolved challenges in scientific mention detection. SciDMT serves as a
robust benchmark for the research community, encouraging the development of innovative models to further the field
of scientific information extraction
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Scientific Meetings for All
Two organizations found ways to be more intentional about encouraging participation by a diverse spectrum of attendees at scientific meetings—the scientific community can learn from their experiences.
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- Award ID(s):
- 1812997
- PAR ID:
- 10155505
- Date Published:
- Journal Name:
- Eos
- Volume:
- 101
- ISSN:
- 2324-9250
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2020EO138951
