CNN In NLP: Top 5 Types & How To Tutorial For Text Classification With TensorFlow

by | Jan 10, 2023 | Machine Learning, Natural Language Processing

Convolutional Neural Networks (CNN) are a type of deep learning model that is particularly well-suited for tasks that involve working with structured data, such as images, audio, or text in NLP. They are called “convolutional” because they use a mathematical operation called convolution to process the input data.

This article covers the most common types of CNN used in NLP and an example in Tensorflow to inspire you to create your own.

What is a CNN in NLP?

A common type of neural network used for tasks involving natural language processing is the CNN (Convolutional Neural Network). It works particularly well when dealing with data sequences, like text. CNNs can be used in NLP to perform language modelling, machine translation, and text classification.

Several filters are applied for CNNs to learn to recognise patterns and features within the input data. Then, predictions or decisions are made using these patterns and features.

CNN in NLP look for patterns within a text.

CNNs look for patterns in features and then use these to make predictions.

CNNs have the benefit of being able to account for the context of the input data, which is crucial in many NLP tasks. A word’s meaning, for instance, may differ depending on the words that come before and after it when classifying a passage of text.

CNNs can understand this context because they use filters that work over a fixed-size input data window.

In general, CNNs are a helpful tool for NLP tasks, and they have been used to get cutting-edge results on many benchmarks.

How does a CNN work?

A CNN applies several filters to the data to find patterns and features in the input data. Most of the time, the filters are put together in one or more “convolutional layers,” which are the CNN’s core parts.

Each convolutional layer applies a set of filters to the input data, and the output of the filters is then passed on to the following layer after passing through a non-linear activation function. The CNN can learn more complex patterns and features by using the activation function to make the network less linear.

Typically, the filters are small, square matrices that scan through the input data in search of patterns and features specific to the task the CNN is being trained to perform.

For instance, the filters could be used in a text classification task to find words or word groups that characterise a specific class.

After the output of the convolutional layers, one or more “fully-connected” layers are often used to make predictions or decisions based on the patterns and features found by the CNN.

In general, a CNN operates by using filters to discover patterns and features within the input data and then using those patterns and features to generate predictions or decisions.

Types of CNN in NLP

The CNN (Convolutional Neural Network) type you choose will depend on the particular requirements of your task. Many different CNN types can be used for natural language processing tasks. Here are a few examples of CNNs that are frequently employed.

1. 1D CNNs

1D CNNs, are a subtype of CNN created specifically to process 1D data sequences, like text. They are frequently used for language modelling, machine translation, and other natural language processing tasks like text classification.

1D CNNs function by applying several filters to the input data to find patterns and features. Most of the time, the filters are small square matrices that look through the data for features and patterns typical of the task the CNN is being trained to do.

One benefit of 1D CNNs is their capacity to consider the input data’s context, which is crucial for many NLP tasks. A word’s meaning, for instance, may differ depending on the words that come before and after it when classifying a passage of text. 1D CNNs can get this context by using filters that work over a fixed-size window of the data they are given.

In general, 1D CNNs are an effective tool for NLP tasks and have been applied to obtain cutting-edge outcomes on numerous benchmarks.

In addition, they are perfect for tasks that involve working with data sequences, like text, because they can keep track of the data’s context and dependencies.

2. 2D CNNs

Convolutional Neural Networks, or 2D CNNs, are a subtype of CNN that work with 2D data like images. They can be used for tasks that require both text and images, like image captioning or visual question answering, even though they are not frequently used for natural language processing tasks.

2D CNNs function by applying several filters to the input data to find patterns and features. Most of the time, the filters are small square matrices that look through the input data for patterns and features specific to the task the CNN is being trained to do.

The ability to account for the spatial relationships within the data, which is crucial for tasks like image classification or object detection, is one benefit of 2D CNNs.

In addition, they can learn more complex patterns and features because they can also find local dependencies in the data.

The ability to capture spatial relationships and local dependencies within the data make 2D CNNs an effective tool for tasks involving working with 2D data, such as images. They can be used for tasks involving text and images but are only sometimes used for NLP tasks.

3. Temporal CNNs

Time-series data or natural language are examples of sequential data with temporal dependencies that can be processed using Temporal CNNs (Convolutional Neural Networks), a variant of 1D CNNs. They are frequently employed for natural language processing tasks, like speech recognition and language modelling.

Temporal CNNs apply several filters to the input data to find patterns and other features. Most of the time, the filters are small square matrices that look through the input data for patterns and features specific to the task the CNN is being trained to do.

The ability to account for the temporal dependencies within the data, which is crucial for tasks like speech recognition or language modelling, is one benefit of temporal CNNs. In addition, they can learn more complex patterns and features because they can also identify the local dependencies present in the data.

Overall, temporal CNNs are valuable for tasks requiring interaction with temporally dependent sequential data, such as time series or natural language. In addition, they are well suited for tasks like speech recognition or language modelling because they can capture the temporal and local dependencies within the data.

4. Dynamic CNNs

Convolutional neural networks (CNNs) that are dynamically trained can handle input sequences with variable lengths, like text. Therefore, they are frequently employed in tasks involving natural language processing where the input text size can vary greatly, such as machine translation or language modelling.

Dynamic CNNs apply several filters to the input data to find patterns and features. Most of the time, the filters are small square matrices that look through the input data for patterns and features specific to the task the CNN is being trained to do.

Dynamic CNNs have the advantage of handling variable-length input sequences, which makes them suitable for tasks like language modelling or machine translation, where the input text length can vary significantly.

In addition, they can learn more complex patterns and features because they can also identify the local dependencies present in the data.

Dynamic CNNs are an effective tool for tasks that deal with variable-length input sequences, such as text. In addition, they are well suited for tasks like machine translation or language modelling because they can handle variable-length inputs and capture the local dependencies within the data.

How to do text classification with CNN in NLP

NLP frequently involves the task of text classification, and CNN (Convolutional Neural Networks) can be used to produce cutting-edge results on a variety of benchmarks.

Typically, the first step in using a CNN to classify text is to transform the text into a numerical representation that can be fed into the network. There are many ways to do this, but the most common ones are to represent the text as a series of embedded words or characters or to encode it as a series of numbers using a “one-hot” encoding or a “bag of words” representation.

The text can be fed into CNN once it has been transformed into a numerical representation. After that, the CNN will apply several filters to the input data to find patterns and features in the text representative of the class you are attempting to predict.

The final prediction or decision is typically reached after passing the output of the CNN through one or more fully-connected layers. The fully connected layers may also contain different characteristics or details about the input text, such as its length or the presence of specific words or phrases.

In general, text classification with a CNN entails numerically representing the text, feeding it to the CNN, and then using the recognised patterns and features to make a prediction or decision.

Python example of CNN in NLP with TensorFlow

Here is a simple example of text classification using a CNN in Python.

import tensorflow as tf
from tensorflow.keras.layers import Embedding, Conv1D, MaxPooling1D, Flatten, Dense

# Define the model
model = tf.keras.Sequential()

# Add an embedding layer to convert the text into numerical representations
model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))

# Add one or more convolutional layers
model.add(Conv1D(filters=num_filters, kernel_size=filter_size, activation='relu'))
model.add(MaxPooling1D())

# Flatten the output of the convolutional layers and add a fully-connected layer
model.add(Flatten())
model.add(Dense(units=hidden_dim, activation='relu'))

# Add the output layer
model.add(Dense(units=num_classes, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=num_epochs, validation_data=(x_val, y_val))

This code defines a simple CNN model for text classification in TensorFlow using the tf.keras API. The model consists of an embedding layer to convert the text into numerical representations, one or more convolutional layers to identify patterns and features in the text, and a fully-connected layer to make the final prediction.

The model is then compiled and trained using the fit method. The x_train and y_train variables should contain the input text and corresponding labels for the training data, and the x_val and y_val variables should include the input text and labels for the validation data.

This is a simple example, and you may need to modify the code depending on the specific requirements of your task.

For example, you may use a different type of embedding or add more layers to the model.

Conclusion

Convolutional neural networks, or CNN, are effective tools for tasks involving natural language processing (NLP). They can capture the context and dependencies within the data and are particularly well-suited for working with data sequences, such as text. The choice of which CNN type to use will depend on the specific requirements of your task. There are many different CNN types that can be used for NLP tasks, including 1D CNNs, 2D CNNs, temporal CNNs, and dynamic CNNs. CNNs are an essential component of any NLP practitioner’s toolkit because they have been used to produce cutting-edge outcomes on numerous NLP benchmarks.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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