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Top rated natural language processing books

Here are some top-rated Natural Language Processing (NLP) books, in no particular order:

  1. "Natural Language Processing (almost) from Scratch" by Collobert et al.: This book provides a comprehensive introduction to NLP, covering topics such as text preprocessing, word representations, and sequence labeling.
  2. "Deep Learning for Natural Language Processing" by Yoav Goldberg: This book focuses on the application of deep learning techniques to NLP tasks, including text classification, sentiment analysis, and machine translation.
  3. "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper: This book provides a practical introduction to NLP using Python, covering topics such as text processing, tokenization, and sentiment analysis.
  4. "Speech and Language Processing" by Dan Jurafsky and James H. Martin: This book is a comprehensive introduction to NLP and speech processing, covering topics such as phonetics, phonology, and dialogue systems.
  5. "Natural Language Understanding" by James Allen: This book provides a detailed introduction to NLP, covering topics such as syntax, semantics, and discourse processing.
  6. "Deep Learning for Natural Language Processing: A Primer" by Sudeshna Sarkar: This book provides an introduction to deep learning techniques for NLP, covering topics such as word embeddings, recurrent neural networks, and attention mechanisms.
  7. "Natural Language Processing: A Machine Learning Approach" by Mehryar Mohri, Fernando Pereira, and Chris Manning: This book provides a comprehensive introduction to NLP from a machine learning perspective, covering topics such as text classification, clustering, and topic modeling.
  8. "Natural Language Generation in Interactive Systems" by Amanda Stent and Srinivas Bangalore: This book provides an introduction to natural language generation, covering topics such as dialogue systems, chatbots, and language generation for interactive systems.
  9. "Applied Natural Language Processing with Python" by Benjamin Baka, Tony Ojeda, and Ian Piper: This book provides a practical introduction to NLP using Python, covering topics such as text preprocessing, sentiment analysis, and topic modeling.
  10. "NLP for Python Developers" by Swapnil Bhartiya: This book provides an introduction to NLP for Python developers, covering topics such as text processing, sentiment analysis, and language modeling.

These books are highly rated and well-regarded in the NLP community, and are suitable for both beginners and experienced practitioners.

Rating sources:

Note: The ratings and recommendations are subject to change over time, and this list is not exhaustive.

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