Natural Language Processing and Computational Linguistics (eBook)

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.

This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.

You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.

This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

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Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.About This BookDiscover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and KerasHands-on text analysis with Python, featuring natural language processing and computational linguistics algorithmsLearn deep learning techniques for text analysisWho This Book Is ForThis book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!What You Will LearnWhy text analysis is important in our modern ageUnderstand NLP terminology and get to know the Python tools and datasetsLearn how to pre-process and clean textual dataConvert textual data into vector space representationsUsing spaCy to process textTrain your own NLP models for computational linguisticsUse statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learnEmploy deep learning techniques for text analysis using KerasIn DetailModern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.Style and approachThe book teaches NLP from the angle of a practitioner as well as that of a student. This is a tad unusual, but given the enormous speed at which new algorithms and approaches travel from scientific beginnings to industrial implementation, first principles can be clarified with the help of entirely practical examples.