About the Technology

Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning. These breakthroughs, including recognizing patterns, inferring meaning from context, and determining emotional tone, are radically improving modern daily conveniences like web searches, social media feeds, and interactions with voice assistants. And they’re transforming the business world too!

A goldmine of unstructured textual data already exists, largely untapped simply because it doesn’t follow any predefined format. NLP is poised to conquer that data with its impressive abilities to scan for keywords and phrases and discern sentiment and preferences. And as the big data trend continues, opportunities to capitalize on the benefits of NLP abound as efforts are being made to ensure data is increasingly user-friendly. What’s more, this game-changing tech can dovetail with your business apps, offering potential for automated summaries, chatbots with near-human responses, and search that practically reads the user’s mind. All this is possible when deep learning meets natural language processing!

About the book

Deep Learning for Natural Language Processing teaches you to apply state-of-the-art deep learning approaches to natural language processing tasks. You’ll learn key NLP concepts like neural word embeddings, auto-encoders, part-of-speech tagging, parsing, and semantic inference. Then you’ll dive deeper into advanced topics including deep memory-based NLP, linguistic structure, and hyperparameters for deep NLP. Along the way, you’ll pick up emerging best practices and gain hands-on experience with a myriad of examples, all written in Python and the powerful Keras library. By the time you’re done reading this invaluable book, you’ll be solving a wide variety of NLP problems with cutting-edge deep learning techniques!

What's inside

• An overview of NLP and deep learning
• One-hot text representations
• Word embeddings
• Models for textual similarity
• Sequential NLP
• Semantic role labeling
• Deep memory-based NLP
• Linguistic structure
• Hyperparameters for deep NLP

About the reader

For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required.

About the author

Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist (machine learning, NLP) at TNO.