Natural Language Processing in Action: Understanding,analyzing,and generating text with Python
Authors: Hobson Lane – Hannes Hapke – Cole Howard
ISBN-10: 1617294632
ISBN-13: 9781617294631
Edition 版次: 1
Publication Date 出版日期: 2019-04-14
Print Length 页数: 544 pages
Book Description
By finelybook
Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI.
Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people,meaningful resume-to-job matches,superb predictive search,and automatically generated document summaries—all at a low cost. New techniques,along with accessible tools like Keras and TensorFlow,make professional-quality NLP easier than ever before.
Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it,you’ll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks,modern deep learning algorithms,and generative techniques as you tackle real-world problems like extracting dates and names,composing text,and answering free-form questions.
What’s inside
Some sentences in this book were written by NLP! Can you guess which ones?
Working with Keras,TensorFlow,gensim,and scikit-learn
Rule-based and data-based NLP
Scalable pipelines
Part 1. Wordy machines
Chapter 1. Packets of thought(NLP overview)
Chapter 2. Build your vocabulary(word tokenization)
Chapter 3. Math with words(TF-IDF vectors)
Chapter 4. Finding meaning in word counts(semantic analysis)
Part 2. Deeper learning(neural networks)
Chapter 5. Baby steps with neural networks(perceptrons and backpropagation)
Chapter 6. Reasoning with word vectors (Word2vec)
Chapter 7. Getting words in order with convolutional neural networks (CNNs)
Chapter 8. Loopy (recurrent) neural networks (RNNs)
Chapter 9. Improving retention with long short-term memory networks
Chapter 10. Sequence-to-sequence models and attention
Part 3. Getting real(real-world NLP challenges)
Chapter 11. Information extraction (named entity extraction and question answering)
Chapter 12. Getting chatty(dialog engines)
Chapter 13. Scaling up (optimization,parallelization,and batch processing)
