Enriching news events with meta-knowledge information
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2331
- Type
- D - Journal article
- DOI
-
10.1007/s10579-016-9344-9
- Title of journal
- Language Resources and Evaluation
- Article number
- -
- First page
- 409
- Volume
- 51
- Issue
- 2
- ISSN
- 1574-020X
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
https://e-space.mmu.ac.uk/607778/
- 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
-
3
- Research group(s)
-
A - Data Science
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents one of the first large scale applications of meta-knowledge annotation to general text, specifically news events. It proved the viability of this approach for non-domain specific texts, and provided a rare manually annotated gold-standard dataset for future research, paving the way for complex NLP tasks like uncertainty detection (Zerva et al., 2017) and hypothesis detection (Shardlow et al., 2018), which subsequently led to a successful EPSRC fellowship on detection of fake medical news at the National Centre for Text Mining (Chrysoula Zerva chrysoula.zerva@manchester.ac.uk).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -