Machine Learning Support for EU Funding Project Categorization. (19th March 2019)
- Record Type:
- Journal Article
- Title:
- Machine Learning Support for EU Funding Project Categorization. (19th March 2019)
- Main Title:
- Machine Learning Support for EU Funding Project Categorization
- Authors:
- Zamazal, Ondřej
- Abstract:
- Abstract: European Union reallocates its money to their member states using different kinds of funding. EU member states categorize EU funding projects using their own categorization system. While EU prepared an integrated European categorization system, many EU members do not use it in their reports. This hinders a straightforward fiscal analysis. The article aims at an automatic support for categorization of EU funding projects by Machine Learning. The experiments showed that Support Vector Machines (SVM) is the top performance Machine Learning algorithm for this task. We experimented with the SVM classifier and the results disclosed that by employing this approach we can classify EU funding projects using a lexical description better than a baseline (i.e. the classification to a major class). Further, we experienced that the approach using the natural language translator outperforms the approach using the word sense disambiguation. Finally, we investigated the influence of the length of project description on the performance of the classifier. The results showed that while there was a positive correlation between the length of project description and the classifier performance for project descriptions in English, in the case of project description in Non-English languages the classifier performed better for shorter project descriptions. In future, we plan to build a new online application which would use the classifier on the back-end and a user would get a categoryAbstract: European Union reallocates its money to their member states using different kinds of funding. EU member states categorize EU funding projects using their own categorization system. While EU prepared an integrated European categorization system, many EU members do not use it in their reports. This hinders a straightforward fiscal analysis. The article aims at an automatic support for categorization of EU funding projects by Machine Learning. The experiments showed that Support Vector Machines (SVM) is the top performance Machine Learning algorithm for this task. We experimented with the SVM classifier and the results disclosed that by employing this approach we can classify EU funding projects using a lexical description better than a baseline (i.e. the classification to a major class). Further, we experienced that the approach using the natural language translator outperforms the approach using the word sense disambiguation. Finally, we investigated the influence of the length of project description on the performance of the classifier. The results showed that while there was a positive correlation between the length of project description and the classifier performance for project descriptions in English, in the case of project description in Non-English languages the classifier performed better for shorter project descriptions. In future, we plan to build a new online application which would use the classifier on the back-end and a user would get a category recommendation on the front-end using a visualization of the EU categorization system. … (more)
- Is Part Of:
- Computer journal. Volume 62:Number 11(2019)
- Journal:
- Computer journal
- Issue:
- Volume 62:Number 11(2019)
- Issue Display:
- Volume 62, Issue 11 (2019)
- Year:
- 2019
- Volume:
- 62
- Issue:
- 11
- Issue Sort Value:
- 2019-0062-0011-0000
- Page Start:
- 1684
- Page End:
- 1694
- Publication Date:
- 2019-03-19
- Subjects:
- EU funding projects -- open fiscal data -- categorization -- RDF code list -- machine learning
Computers -- Periodicals
005.1 - Journal URLs:
- http://comjnl.oxfordjournals.org/ ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/comjnl/bxz021 ↗
- Languages:
- English
- ISSNs:
- 0010-4620
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.060000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12374.xml