Identification of Multi-Focal Questions in Question and Answer Reports

摘要

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%.