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Discovering topics in text datasets by visualizing relevant words
Franziska Horn, Leila Arras, Grégoire Montavon +2
When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents. In this paper we show how this can be achieved by using a clus…
Exploring text datasets by visualizing relevant words
Franziska Horn, Leila Arras, Grégoire Montavon +2
When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms. However, to the best of…
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller +1
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forwar…
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Leila Arras, Franziska Horn, Grégoire Montavon +2
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatic…
Explaining Predictions of Non-Linear Classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon +2
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we…