Summary of the paper

Title Ensemble Classification of Grants using LDA-based Features
Authors Yannis Korkontzelos, Beverley Thomas, Makoto Miwa and Sophia Ananiadou
Abstract Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making. Automating this classification process would save time and effort, providing the accuracy of the classifications is maintained. We employ five classification models to classify a set of BBSRC-funded research grants in 21 research topics based on unigrams, technical terms and Latent Dirichlet Allocation models. To boost precision, we investigate methods for combining their predictions into five aggregate classifiers. Evaluation confirmed that ensemble classification models lead to higher precision.It was observed that there is not a single best-performing aggregate method for all research topics. Instead, the best-performing method for a research topic depends on the number of positive training instances available for this topic. Subject matter experts considered the predictions of aggregate models to correct erroneous or incomplete manual assignments.
Topics Document Classification, Text categorisation, Text Mining, Tools, Systems, Applications
Full paper Ensemble Classification of Grants using LDA-based Features
Bibtex @InProceedings{KORKONTZELOS16.387,
  author = {Yannis Korkontzelos and Beverley Thomas and Makoto Miwa and Sophia Ananiadou},
  title = {Ensemble Classification of Grants using LDA-based Features},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
Powered by ELDA © 2016 ELDA/ELRA