Automatic Question Classifiers: A Systematic Review

Question classification is a key point in many applications, such as Question Answering (QA, e.g., Yahoo! Answers), Information Retrieval (IR, e.g., Google search engine), and E-learning systems (e.g., Bloom's tax. classifiers). This paper aims to carry out a systematic review of the literature on automatic question classifiers and the technology directly involved. Automatic classifiers are responsible for labeling a certain evaluation item using a type of categorization as a selection criterion. The analysis of 80 primary studies previously selected revealed that SVM is the main algorithm of the Machine Learning used, while BOW and TF-IDF are the main techniques for feature extraction and selection, respectively. According to the analysis, the taxonomies proposed by Li and Roth and Bloom were the most used ones for the classification criteria, and Accuracy/Precision/Recall/F1-score were proven to be the most used metrics. In the future, the objective is to perform a meta-analysis with the studies that authorize the availability of their data.