Papers › High Accuracy Rule-based Question Classification using Question Syntax and Semantics
High Accuracy Rule-based Question Classification using Question Syntax and Semantics
Harish Tayyar Madabushi, Mark Lee
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.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Classification | TREC-50 | Rules | Error | 2.8 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections