Book Image

Natural Language Processing with TensorFlow

By : Motaz Saad, Thushan Ganegedara
Book Image

Natural Language Processing with TensorFlow

By: Motaz Saad, Thushan Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow

Using TensorFlow RNN API with pretrained GloVe word vectors

So far, we have implemented everything from scratch in order to understand the exact underlying mechanisms of such a system. Here we will discuss how to use the TensorFlow RNN API along with pretrained GloVe word vectors in order to reduce both the amount of code and learning for the algorithm. This will be available as an exercise in the lstm_image_caption_pretrained_wordvecs_rnn_api.ipynb notebook found in the ch9 folder.

We will first discuss how to download the word vectors and then discuss how to load only the relevant word vectors from the downloaded file, as the vocabulary size of the pretrained GloVe vectors is around 400,000 words, whereas ours is just 18,000. Next, we will perform some elementary spelling correction of the captions, as there seems to be a lot of spelling mistakes present. Then we will discuss how we can process the cleaned data using a tf.nn.rnn_cell.LSTMCell module found in the RNN API.

Loading GloVe word...