Question type classification and its application to a question answering system

2005 ◽  
Author(s):  
June-Jei Kuo ◽  
Kuei-Kuang Lin ◽  
Hsin-Hsi Chen ◽  
Cheng-Hsuan Kao ◽  
Bor-Shen Lin
Keyword(s):  
Question Answering ◽  
Question Type ◽  
Question Answering System ◽  
Type Classification

A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering

2017 ◽  
Vol 56 (03) ◽  
pp. 209-216 ◽  
Author(s):  
Said Ouatik El Alaoui ◽  
Mourad Sarrouti
Keyword(s):  
Machine Learning ◽  
Question Answering ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Question Type ◽  
Support Vector ◽  
Learning Approaches ◽  
Answer Extraction ◽  
Improved Performance ◽  
Type Classification

SummaryBackground and Objective: Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary.Methods: In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine- learning algorithms. Finally, the class label is predicted using the trained classifiers.Results: Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features’ provider of support vector machine (SVM) lead to the highest accuracy of 89.40%.Conclusion: The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

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