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.
|Title of host publication||Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers|
|Place of Publication||Osaka, Japan|
|Publisher||The COLING 2016 Organizing Committee|
|Number of pages||11|
|Publication status||Published - 1 Dec 2016|