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A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning

, , , , and . Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, page 1--6. (2022)

Abstract

Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.

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