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.
Original language | English |
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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 |
Pages | 1220-1230 |
Number of pages | 11 |
Publication status | Published - 1 Dec 2016 |