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} } |