Adaptive Concept Resolution for document representation and its applications in text mining
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1403
- Type
- D - Journal article
- DOI
-
10.1016/j.knosys.2014.10.003
- Title of journal
- Knowledge-Based Systems
- Article number
- -
- First page
- 1
- Volume
- 74
- Issue
- -
- ISSN
- 0950-7051
- 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
-
4
- Research group(s)
-
A - Artificial Intelligence (AI)
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in KBS, a top journal, this novel method to learn text document representation using an ontology remarkably outperforms state-of-the-art models in both classification (documents with labels) and unsupervised (documents without labels) tasks. The underlying novel model learns concept border for a document which provides tailor-made semantic concept representation from the same domain. The paper makes a major contribution by sharing a new ontology derived from a very popular WordNet known as WordNet-Plus which is further enhanced with Wikipedia entities. As one of the first examples this influenced others, particularly in new document representation learning methodologies and information retrieval.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
