Natural Language Processing(NLP)

The provided code demonstrates how to use word embedding techniques with the embedding layer in Keras. Here are the steps outlined in the code:

• Import necessary libraries, including TensorFlow and Keras.
• Define sentences as a list of strings.
• Specify the vocabulary size (voc_size) and use the one_hot function from Keras to perform one-hot encoding on the sentences.
• Import additional necessary modules from Keras for word embedding.
• Define the maximum length of sentences (sent_length) and pad the sequences using pad_sequences.
• Define the dimension of the embedding space (dim) and create a Sequential model in Keras.
• Add an Embedding layer to the model with the specified vocabulary size, dimension, and input length.
• Compile the model using an optimizer and a loss function.
• Print the summary of the model.
• Test the model by predicting the output for one of the embedded documents.
• This code snippet demonstrates the process of converting words into dense vectors using word embedding techniques, allowing neural networks to process textual data effectively.