2016
COLING
COLING 2016
High Accuracy Rule-based Question Classification using Question Syntax and Semantics
Abstract
AbstractWe 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.
🌱
Topic Pioneer
— Intent Classification
🌉
Interdisciplinary Bridge
— Interdisciplinary and Natural Language Processing
📈
Trend Setter
— Intent Classification
🧭
Keyword Pioneer
— rule-based system
🐣
Hot Topic Early Bird
— semantic analysis
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio