Project Details
Description
Program aims to leapfrog state-of-the-art big data analytics by developing automated technologies to help explain the causes and effects that drive complicated systems
| Status | Finished |
|---|---|
| Effective start/end date | 01/1/17 → 12/31/19 |
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Optimized Machine Learning Methods Predict Discourse Segment Type in Biological Research Articles
Cox, J., Harper, C. A. & de Waard, A., Jan 1 2018, Semantics, Analytics, Visualization - 3rd International Workshop, SAVE-SD 2017, and 4th International Workshop, SAVE-SD 2018, Revised Selected Papers. Osborne, F., Peroni, S., Vahdati, S. & González-Beltrán, A. (eds.). Springer Verlag, p. 95-109 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10959 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
2 Link opens in a new tab Scopus citations -
Automated detection of discourse segment and experimental types from the text of cancer pathway results sections
Burns, G. A. P. C., Dasigi, P., de Waard, A. & Hovy, E. H., Jan 1 2016, In: Database : the journal of biological databases and curation. 2016, baw122.Research output: Contribution to journal › Article › peer-review
Open Access16 Link opens in a new tab Scopus citations -
Cycles of scientific investigation in discourse: Machine reading methods for the primary research contributions of a paper
Burns, G. A., De Waard, A., Dasigi, P. & Hovy, E. H., Jan 1 2016, In: CEUR Workshop Proceedings. 1747Research output: Contribution to journal › Conference article › peer-review