Summary of the paper

Title A Modular System for Rule-based Text Categorisation
Authors Marco Del Tredici and Malvina Nissim
Abstract We introduce a modular rule-based approach to text categorisation which is more flexible and less time consuming to build than a standard rule-based system because it works with a hierarchical structure and allows for re-usability of rules. When compared to currently more wide-spread machine learning models on a case study, our modular system shows competitive results, and it has the advantage of reducing manual effort over time, since only fewer rules must be written when moving to a (partially) new domain, while annotation of training data is always required in the same amount.
Topics Topic Detection & Tracking, Ontologies
Full paper A Modular System for Rule-based Text Categorisation
Bibtex @InProceedings{DELTREDICI14.941,
  author = {Marco Del Tredici and Malvina Nissim},
  title = {A Modular System for Rule-based Text Categorisation},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
Powered by ELDA © 2014 ELDA/ELRA