Analysis of named entity recognition and linking for tweets
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2427
- Type
- D - Journal article
- DOI
-
10.1016/j.ipm.2014.10.006
- Title of journal
- Information Processing & Management
- Article number
- -
- First page
- 32
- Volume
- 51
- Issue
- 2
- ISSN
- 0306-4573
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- 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
-
7
- Research group(s)
-
D - Natural Language Processing
- Citation count
- 118
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first study to benchmark named entity recognition and linking tools developed for newswire text on social media discourse, carried out using a novel entity linking dataset created for and provided with the paper. It is widely used, e.g. by researchers from ETH Zurich (doi.org/10.18653/v1/K18-1050) and Leipzig University (doi.org/10.3233/SW-170286). This work led to successful EU grant applications in the area of social media analysis: TrendMiner (FP7-ICT-287863), SoBigData (H2020-ICT-654024) and SoBigData++ (H2020-ICT-871042). This is the most-cited paper in IP&M in the year of publication and the two previous years (doi.org/10.1016/j.ipm.2014.10.006, Metric options: 1y; 3y).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -