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Chapters on Applied Natural Language Processing with Python will be explained in the following order:
Chapter 1: What Is Natural Language Processing?
It belongs to the artificial intelligence field. The computer system is capable of comprehending spoken or written human language.
Natural language processing history, A summary, applications of deep learning to NLP, a review of machine learning and deep learning, and packages for NLP, machine learning, and deep learning with Python are some of the subjects covered in Chapter 1.
Chapter 2: Review of Deep Learning
It is a subclass of machine learning that behaves like a neural network with more than three layers.
Chapter 2’s topics includes – Vanishing Gradients and Why Recurrent Neural Networks Help to Prevent Them, Toy Example 1: Modelling Stock Returns with the MLP Model, Multilayer Perceptrons and Recurrent Neural Networks, Loss Functions and Backpropagation Recurrent Neural Networks and Long Short-Term Memory, Toy Example 2: Modelling Stock Returns with the RNN Model, Toy Example 3: Modelling Stock Returns with the LSTM Model, Summary About the Author, About the Technical Reviewer, Acknowledgements, and its Introduction are all included in this article.
Chapter 3: Working with Raw Text
It only has alphanumeric characters, like group, block, and string.
A few of the subjects are covered in Chapter 3. – The Bag-of-Words Model (BoW)CountVectorizer, Tokenization and Stop Words, Term Frequency Inverse Document Frequency, Example Problem 1: Spam Detection, Example Problem 2: Classifying Movie Reviews, and Summary are some of the topics covered.
Chapter 4: Topic Modeling and Word Embeddings
Word embeddings indicate comparable content and offer a similar depiction of what they imply. By using topic modelling, the text data is automatically identified and analysed.
Several of topics covered in Chapter 4 include – Non-Negative Matrix Factorization (NMF), Topic Model with Latent Dirichlet Allocation (LDA), and Topic Modelling with LDA on Movie Review DataWord2Vec, Case of a Problem 4.2: Continuous Bag of Words (CBoW) Training a Word Embedding (Skip-Gram), Example Problem 4.2: Global Vectors for Word Representation (GloVe) and Training a Word Embedding (CBoW) Case of a Problem 4.4: Distributed Memory of Paragraph Vectors (PV-DM) using Trained Word Embeddings and LSTMs. Paragraph2Vec Example with Movie, Review Data, and Summary for Example Problem 4.5.
Chapter 5: Text Generation, Machine
Other Common Language Modelling Tasks Include Translation You provide comprehensive knowledge on text generation, machine learning, and other recurrent language modelling problems in this paragraph.
Topics covered in Chapter 5 comprise – The creation of text using LSTMs, BRNNs, and A Name Entity Recognition Tagger, Sequence-to-Sequence Models (Seq2Seq), Question and Answer using Neural Network Models, Summary, Conclusion, and Final Statements are some examples of the techniques used.
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