A Study of Distributed Representations for Figures of Research Articles

Saar Kuzi, ChengXiang Zhai

Abstract: Figures of research articles are entities that can be directly used in many application systems to assist researchers, making the representation of figures a problem worth studying. In this paper, we study the effectiveness of distributed representations, learned using deep neural networks, for figures. We learn representations using both text and image data and compare different model architectures and loss functions for the task. Furthermore, to overcome the lack of training data for the task, we propose and study a novel weak supervision approach for learning embedding vectors and show that it is more effective than using some of the pre-trained neural models as suggested by recent works. Experimental results using figures from the ACL Anthology show that distributed representations for research figures can be more effective than the previously studied bag-of-words representations. Yet, combining the two approaches can further improve performance. Finally, the results also show that the dense representations, while effective in general, can be sensitive to the learning approach used and that using both image data and text and a simple model architecture is the most effective approach.