How Is Convolutional Neural Network (CNN) Used In NLP ?

Convolutional Neural Networks (CNNs) are primarily associated with computer vision tasks, like image recognition, but they're also gaining traction in Natural Language Processing (NLP) due to their ability to effectively process sequential data. In NLP, CNNs are primarily employed for tasks such as text classification, sentiment analysis, and document categorization.

CNNs in NLP operate similarly to their use in computer vision but with text data. Instead of pixels, CNNs process words or characters as input. The core idea is to use convolutional layers to detect patterns or features at different levels of granularity within the text.

Here's how it works:

1. Word Embeddings: Before feeding text data into a CNN, words are typically converted into dense numerical vectors using techniques like Word2Vec or GloVe. This step transforms text into a format suitable for neural networks.

2. Convolutional Layers: CNNs use convolutional layers to scan across the input text, capturing local patterns. Each convolutional filter acts as a feature detector, identifying specific linguistic patterns or combinations of words. For example, it might detect phrases like "not good" or "very happy".

3. Pooling Layers: After convolution, pooling layers aggregate information from the convolutional outputs, reducing dimensionality while retaining important features. Max-pooling, for instance, selects the maximum value from each feature map, highlighting the most salient features.

4. Fully Connected Layers: These layers integrate the features learned by the convolutional and pooling layers, mapping them to the desired output classes. In text classification, for instance, fully connected layers might determine the sentiment of a sentence.

CNNs in NLP have demonstrated effectiveness in various tasks due to their ability to capture local patterns and hierarchical relationships within text data. They can discern important features irrespective of their position in a sentence, making them robust for tasks like sentiment analysis or document classification where context matters. Despite their simplicity, CNNs offer competitive performance and efficiency in NLP tasks, making them a valuable tool in the NLP toolkit.