Learn NLP working through data and understand statistical NLP and deep learning
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.
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.
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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.
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