Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras - Tapa blanda

Reseña del editor

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.

Key Features

• Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras
• Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms
• Learn deep learning techniques for text analysis

Book Description

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.

What you will learn

• Why text analysis is important in our modern age
• Understand NLP terminology and get to know the Python tools and datasets
• Learn how to pre-process and clean textual data
• Convert textual data into vector space representations
• Using spaCy to process text
• Train your own NLP models for computational linguistics
• Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn
• Employ deep learning techniques for text analysis using Keras

Who This Book Is For

This 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!

Table of Contents

1. What is Text Analysis?
2. Python Tips for Text Analysis
3. spaCy's Language Models
4. Gensim – Vectorizing text and transformations and n-grams
5. POS-Tagging and its Applications
6. NER-Tagging and its Applications
7. Dependency Parsing
8. Top Models
9. Advanced Topic Modelling
10. Clustering and Classifying Text
11. Similarity Queries and Summarization
12. Word2Vec, Doc2Vec and Gensim
13. Deep Learning for text
14. Keras and spaCy for Deep Learning
15. Sentiment Analysis and ChatBots

Biografía del autor

Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and has a deep interest in modern text analysis. He works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.