Identification of research hypotheses and new knowledge from scientific literature
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84928984
- Type
- D - Journal article
- DOI
-
10.1186/s12911-018-0639-1
- Title of journal
- BMC Medical Informatics and Decision Making
- Article number
- 46
- First page
- -
- Volume
- 18
- Issue
- -
- ISSN
- 1472-6947
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
A - Computer Science
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper is the first to employ a supervised approach for classifying an event described in scientific text, according to whether it pertains to a research hypothesis or new knowledge.
Keynote (Ananiadou) at the IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, Amman, Jordan, April 2019.
The work described in this paper was applied in the Big Mechanism project funded by the Defense Advanced Research Projects Agency (DARPA)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
