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

这个项目解决了问题分类任务：自动区分不同类型的规范和非规范问题的任务。该项目包括三个部分。其中一个部分侧重于语言信息的提取和收集，这些信息将用作机器学习模型（ML）和视觉分析（VA）技术的特征，用于分类任务。第二部分包括用于ML模型的交互式调整以改进问题分类的VA技术。通过交互式操纵模型的可视化表示，我们使用户能够交互式地适应和改进学习的模型。第三部分涉及使ML技术透明，以便为问题分类生成额外的语言见解。我们的目标是开发新的方法来传达ML模型做出的决策，并为手头的任务提供语言见解。通过追求这些目标，我们通过在计算语言学和VA中开发新的工具和方法来对问题类型进行分析和分类，从而为问题的研究做出贡献。与此同时，我们与研究单位的其他项目进行互动，将LingVis（语言学可视化）开辟的可能性带入各个项目，并将其应用于这些项目所追求的工作。 该项目得益于反馈周期，因为它可以将其他项目对问题结构的见解纳入其本身的分类工作，从而产生可用于其他项目的结果。

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.