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

Harish Tayyar Madabushi; Mark Lee

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

Department of Computer Science

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

43 Citations (SciVal)

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
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

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https://aclanthology.org/C16-1116Licence: CC BY

Cite this

High Accuracy Rule-based Question Classification using Question Syntax and Semantics. / Tayyar Madabushi, Harish; Lee, Mark.
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. Osaka, Japan: The COLING 2016 Organizing Committee, 2016. p. 1220-1230 C16-1116.

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

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M3 - Chapter in a published conference proceeding

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

Abstract

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