# Analyzing and Interpreting Convolutional Neural Networks in NLP

arXiv: https://arxiv.org/abs/1810.09312 · HTML: https://mlsanity.com/paper/1810.09312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Explainable AI: current status and future directions](https://mlsanity.com/paper/2107.07045/) (2021)
- [Model Explainability in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/2106.07410/) (2021)

## Abstract (arXiv)

Convolutional neural networks have been successfully applied to various NLP tasks. However, it is not obvious whether they model different linguistic patterns such as negation, intensification, and clause compositionality to help the decision-making process. In this paper, we apply visualization techniques to observe how the model can capture different linguistic features and how these features can affect the performance of the model. Later on, we try to identify the model errors and their sources. We believe that interpreting CNNs is the first step to understand the underlying semantic features which can raise awareness to further improve the performance and explainability of CNN models.