Identifying Unclear Questions in Community Question Answering Websites
This addresses the issue of unclear questions for users and experts on community Q&A websites, but it is incremental as it builds on existing text classification methods.
The study tackled the problem of identifying unclear questions on community Q&A websites by constructing a novel dataset and proposing a classification approach based on similar questions, finding it to be a viable alternative to state-of-the-art baselines for developing supportive user interfaces.
Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days. However, oftentimes submitted questions are unclear and cannot be answered without further clarification questions by expert community members. This study is the first to investigate the complex task of classifying a question as clear or unclear, i.e., if it requires further clarification. We construct a novel dataset and propose a classification approach that is based on the notion of similar questions. This approach is compared to state-of-the-art text classification baselines. Our main finding is that the similar questions approach is a viable alternative that can be used as a stepping stone towards the development of supportive user interfaces for question formulation.