Natural Language Processing (NLP) using Deep Learning
Learn how to use TensorFlow and Keras for Natural Language Processing powered by Deep Learning
Natural Language Processing (NLP) is a field of Artificial Intelligence that focuses on understanding human language and enabling computers to process, understand and generate natural language. NLP is widely used for text classification, sentiment analysis, machine translation, and many other tasks. With the rise of deep learning, NLP has seen significant improvement in accuracy and efficiency.
Let’s use TensorFlow and Keras, two popular deep learning frameworks, to demonstrate NLP tasks with deep learning.
Tokenization
Tokenization is the first step in NLP, where we break down a sentence or a document into individual words or tokens. In deep learning, we typically use word embeddings to represent words, and tokenization helps us to convert words into numerical representations that can be used by deep learning models.
from keras.preprocessing.text import Tokenizer
texts = ['A cat sitting on the roof', 'Dogs chasing after a cat']
tokenizer = Tokenizer(num_words=100)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
