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.- Anthology ID:
- L14-1715
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/941_Paper.pdf
- DOI:
- Cite (ACL):
- Marco Del Tredici and Malvina Nissim. 2014. A Modular System for Rule-based Text Categorisation. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- A Modular System for Rule-based Text Categorisation (Del Tredici & Nissim, LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/941_Paper.pdf