Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field.
Key features
An overview of NLP and deep learning
Models for textual similarity
Deep memory-based NLP
Semantic role labeling
Sequential NLP
Audience
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 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
Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He's the technical coordinator of two large European Union-funded research security-related projects. He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.