spaCy offers us an easy way to annotate your text data very easily, and with the language model, we annotate your text data with a lot of information – not just tokenizing and whether it is a stop word or not, but also the part of speech, named entity tag, and so on – we can also train these annotating models on our own, giving a lot of power to the language model and processing pipeline! Downloading the models and using virtual environments are also an important part of this process. We will now move on to using our cleaned data in a way that machines can understand us – with vectors, and what kind of Python libraries we would need for the same.
Natural Language Processing and Computational Linguistics
By :
Natural Language Processing and Computational Linguistics
By:
Overview of this book
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.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Free Chapter
What is Text Analysis?
Python Tips for Text Analysis
spaCy's Language Models
Gensim – Vectorizing Text and Transformations and n-grams
POS-Tagging and Its Applications
NER-Tagging and Its Applications
Dependency Parsing
Topic Models
Advanced Topic Modeling
Clustering and Classifying Text
Similarity Queries and Summarization
Word2Vec, Doc2Vec, and Gensim
Deep Learning for Text
Keras and spaCy for Deep Learning
Sentiment Analysis and ChatBots
Other Books You May Enjoy
Index
Customer Reviews