Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts by highlighting the basic building blocks of the natural language processing domain.
The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.
By the end of this book, you will not only have sound knowledge of natural language processing, but also be able to select the best text preprocessing and neural network models to solve a number of NLP issues.
Understand various preprocessing techniques for solving deep learning problems
Build a vector representation of text using word2vec and GloVe
Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
Build a machine translation model in Keras
Develop a text generation application using LSTM
Build a trigger word detection application using an attention model