A soup-to-nuts introduction to natural language processing
A introduction to 4 language processing frameworks, each with their strengths and particular application area
An introduction to NLP with deep learning and Keras.

Book Description

Natural language processing is like the secret sauce of artificial intelligence and machine learning. It is basically impossible to process unstructured data without it, and we should not forget that all data starts its existence as unstructured or semi-structured data.

This book is a broad introduction to natural language processing, travelling through data cleaning and computational linguistics, before presenting the more sophisticated areas of statistical NLP and deep learning. Correspondingly, the author emphasizes that the best frameworks should be used for applications they are best suited for, for instance, GenSim for topic modeling or Keras for deep learning.

In all, Beginning Natural Language Processing does not go deep into libraries and frameworks, but tries to get the reader fired up about the power of natural language processing.

What you will learn

You will learn to clearly distinguish basic NLP terminology, e.g. you will know the difference between computational linguistics and natural language processing
You will learn how to prepare text and corpora for analysis using computational linguistic methods
Among other things, you will study the role of deep learning, particularly Keras, in the context of unstructured data, a role where RNNs and similar methods excel
You will get to know the power of SpaCy in general NLP, a new open source framework focusing on natural language processing
And you will learn how to apply GenSim for topic modeling and Scikit
Learn for basic text processing

Who This Book Is For

Fluency in Python is assumed. Basic statistics is helpful. Given that this book introduces natural language processing from first principles, it helps, although it is not a requirement, to be familiar with basic linguistics.

About the Author

Bhargav Srivinasa-Desikan is a student researcher working for INRIA in Lille, France. He is part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation and data visualization. He also contributes to open source machine learning projects, particularly dynamic topic models for Gensim.

"About this title" may belong to another edition of this title.

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