Author: Bhargav Srinivasa-Desikan
Pub Date: 2018
Size: 11 Mb
Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras
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.
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