Generating Linguistic Insights in Question Classification throughCombining Explainable Machine Learning and Visualization

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This project tackles the question classification task: the task of automatically distinguishing between different types of canonical and non-canonical questions. The project encompasses three parts. One part focuses on the extraction and collection of linguistic information to be used as features for Machine Learning models (ML) and Visual Analytics (VA) techniques for the classification task. The second part includes VA techniques for the interactive adjustment of the ML models to improve question classification. Through an interactive manipulation of the model's visual representation we enable the user to interactively adapt and improve the learned model. The third part deals with making the ML techniques transparent in order to generate additional linguistic insights for question classification. We aim to develop novel methods to communicate decisions made by a ML model and provide linguistic insights for the task at hand. By pursuing these goals we are contributing to the research of questions by developing novel tools and methodology within computational linguistics and VA for the analysis and classification of question types. At the same time, we interact with other projects of the Research Unit in terms of carrying the possibilities opened up by LingVis (Visualization for Linguistics) into individual projects and applying them towards the work pursued by these projects. This project benefits from a feedback cycle in that it can integrate insights on the structure of questions coming from other projects into its own classificatory work and can in turn produce results that can then be carried into the other projects.