High Accuracy Rule-based Question Classification using Question Syntax and Semantics

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

38 Citations (SciVal)


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 languageEnglish
Title of host publicationProceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Place of PublicationOsaka, Japan
PublisherThe COLING 2016 Organizing Committee
Number of pages11
Publication statusPublished - 1 Dec 2016


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