文章基本信息

标题：QUESTION CLASSIFICATION USING STATISTICAL APPROACH: A COMPLETE REVIEW
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作者：ANBUSELVAN SANGODIAH ; MANORANJITHAM MUNIANDY ; LIM EAN HENG 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：71
期号：3
出版社：Journal of Theoretical and Applied
摘要：In recent days, question classification is growing in popularity as it has an important role in question answering systems, information retrieval and it can be used in a wide range of other domains. The main aim of question classification is to accurately assign labels to questions based on expected answer type. Past research works have relied on matching questions against hand-crafted rules. However, rules require enormous effort to create and often suffer from being too specific. A great deal of current research works on question classification is based on statistical approach to overcome these issues by employing machine learning techniques such as Support Vector Machine and Artificial Neural Network. This paper presents an updated literature survey of current methods or approaches for question classification in the areas of question answering systems, information retrieval and educational environment. Question classification involving other languages besides English has also been examined.
关键词：Question Classification; Machine Learning; Semantic Features; Syntactic Features; Support Vector Machine