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 | 26th International Conference on Computational Linguistics (COLING 2016) |
| Publisher | Association for Computational Linguistics, ACL |
| Pages | 1220-1230 |
| Number of pages | 11 |
| ISBN (Print) | 978-4-87974-702-0 |
| Publication status | Published - 16 Dec 2016 |
| Event | 26th International Conference on Computational Linguistics (COLING 2016) - Osaka, Japan Duration: 11 Dec 2016 → 16 Dec 2016 |
Conference
| Conference | 26th International Conference on Computational Linguistics (COLING 2016) |
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| Country/Territory | Japan |
| City | Osaka |
| Period | 11/12/16 → 16/12/16 |