High Accuracy Rule-based Question Classification using Question Syntax and Semantics
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.
PDF Abstract COLING 2016 PDF COLING 2016 AbstractDatasets
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Text Classification | TREC-50 | Rules | Error | 2.8 | # 1 |