# Neural Networks as Explicit Word-Based Rules

## TL;DR

This paper interprets convolutional neural networks for NLP as explicit word-based rules, enabling understanding and recovery of model performance through rule extraction.

## Contribution

It introduces a method to interpret CNN weights in NLP as explicit rules, bridging the gap between neural models and human-readable linguistic patterns.

## Key findings

- CNNs can be effectively interpreted as word-based rules.
- The extracted rules can recover the original model's performance.
- This approach enhances interpretability of NLP neural models.

## Abstract

Filters of convolutional networks used in computer vision are often visualized as image patches that maximize the response of the filter. We use the same approach to interpret weight matrices in simple architectures for natural language processing tasks. We interpret a convolutional network for sentiment classification as word-based rules. Using the rule, we recover the performance of the original model.