Descriptive Document Clustering via Discriminant Learning in a Co-Embedded Space of Multilevel Similarities
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 40100279
- Type
- D - Journal article
- DOI
-
10.1002/asi.23374
- Title of journal
- Journal of the Association for Information Science and Technology
- Article number
- -
- First page
- 106
- Volume
- 67
- Issue
- 1
- ISSN
- 2330-1635
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- 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
-
3
- Research group(s)
-
A - Computer Science
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper is the first work to successfully address the issue of weak semantic relatedness in descriptive clustering.
Enabled funding EU H2020 CROSSMINER (732223) totalling around EUR4,500,000.
Enabled follow-on papers:
- IEEE TKDE (DOI: 10.1109/TKDE.2017.2781721, acceptance rate 14%)
- EACL (doi.org/10.18653/v1/e17-1093, acceptance rate 27%).
Integrated in text mining service RobotAnalyst (http://www.nactem.ac.uk/robotanalyst/) offered by the UK National Centre for Text Mining."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
