EBBE-Text: Explaining Neural Networks by Exploring Text Classification Decision Boundaries

Abstract

While neural networks (NN) have been successfully applied to many NLP tasks, the way they function is often difficult to interpret. In this article, we focus on binary text classification via NNs and propose a new tool, which includes a visualization of the decision boundary and the distances of data elements to this boundary. This tool increases the interpretability of NN. Our approach uses two innovative views: (1) an overview of the text representation space and (2) a local view allowing data exploration around the decision boundary for various localities of this representation space. These views are integrated into a visual platform, EBBE-Text, which also contains state-of-the-art visualizations of NN representation spaces and several kinds of information obtained from the classification process. The various views are linked through numerous interactive functionalities that enable easy exploration of texts and classification results via the various complementary views. A user study shows the effectiveness of the visual encoding and a case study illustrates the benefits of using our tool for the analysis of the classifications obtained with several recent NNs and two datasets.

A Novel Text Classification Model Combining Time Correlation Principle and Rough Set Theory

“…They can also publish their information to others online, and this open and accessible way of information sharing and circulation has brought about a massive accumulation of knowledge. This open and accessible way of sharing and circulation has brought about an enormous collection of information [2]. While we get the convenience, we are also overwhelmed by the vast amount of data, making it more challenging to find the required content quickly and effectively.…”