Science / Technology
8 papers with code • 0 benchmarks • 0 datasets
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Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context
Enabling computers to access the information encoded in mathematical formulae requires machine-readable formats that can represent both the presentation and content, i. e., the semantics, of formulae.
Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction
The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community.
Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula.
We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions.
We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55, 485 papers in 10 STM domains.
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