# Explaining Predictions of Non-Linear Classifiers in NLP Source: [Explaining Predictions of Non-Linear Classifiers in NLP]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:22:26.903Z Authors: Leila Arras, Franziska Horn, Gr\\'egoire Montavon, Klaus-Robert M\\"uller, Wojciech Samek Published: 2016-06-23T12:53:31.000Z ## Abstract 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 apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a "word deleting" perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()