Identification of Multi-Focal Questions in Question and Answer Reports

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• 作者：Mona Mohamed Zaki Ali (18) (19)
  Goran Nenadic (18)
  Babis Theodoulidis (20)
  
• 关键词：Question Classification ; Question Analysis ; Content Analysis ; Data Quality ; Text Mining ; Data Mining ; Machine Learning ; Rule ; based Methods
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8455
• 期：1
• 页码：126-137
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• 作者单位：Mona Mohamed Zaki Ali (18) (19)
  Goran Nenadic (18)
  Babis Theodoulidis (20)
  
  18. School of Computer Science, The University of Manchester, Manchester, UK
  19. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
  20. Manchester Business School, The University of Manchester, Manchester, UK
  
• ISSN：1611-3349

文摘

A significant amount of business and scientific data is collected via question and answer reports. However, these reports often suffer from various data quality issues. In many cases, questionnaires contain a number of questions that require multiple answers, which we argue can be a potential source of problems that may lead to poor-quality answers. This paper introduces multi-focal questions and proposes a model for identifying them. The model consists of three phases: question pre-processing, feature engineering and question classification. We use six types of features: lexical/surface features, Part-of-Speech, readability, question structure, wording and placement features, question response type and format features and question focus. A comparative study of three different machine learning algorithms (Bayes Net, Decision Tree and Support Vector Machine) is performed on a dataset of 150 questions obtained from the Carbon Disclosure Project, achieving the accuracy of 91%.