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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Colleges, School and Institutes

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.

Details

Original languageEnglish
Title of host publication26th International Conference on Computational Linguistics (COLING 2016)
Publication statusPublished - 16 Dec 2016
Event26th International Conference on Computational Linguistics (COLING 2016) - Osaka, Japan
Duration: 11 Dec 201616 Dec 2016

Conference

Conference26th International Conference on Computational Linguistics (COLING 2016)
CountryJapan
CityOsaka
Period11/12/1616/12/16