Abstract
We present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to Concepts. We achieve an accuracy of 97.2%, close to a 6 point improvement over the previous State of the Art of 91.6%. Additionally, we believe that machine learning algorithms can be applied on top of this method to further improve accuracy.- Anthology ID:
- C16-1116
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1220–1230
- Language:
- URL:
- https://aclanthology.org/C16-1116
- DOI:
- Cite (ACL):
- Harish Tayyar Madabushi and Mark Lee. 2016. High Accuracy Rule-based Question Classification using Question Syntax and Semantics. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1220–1230, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- High Accuracy Rule-based Question Classification using Question Syntax and Semantics (Tayyar Madabushi & Lee, COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/C16-1116.pdf