For our experiments, we will be using the IMDB sentiment classification task. This is quite the small dataset - we are using it for the convenience of loading it and using it, as it is easily available via Keras. It is very important to understand here that for datasets of the size we are using, it is not the best idea to use a Deep Neural Network for classification - indeed, we might even get better results with a simple bag of words followed by a Support Vector Machine (SVM) doing the classification. The purpose of the following examples is to rather allow the user to understand how to construct a neural network using Keras, and how to make predictions using it. The fine tuning of the neural network and studying its hyperparameters is a different ball game altogether and is not the focus of this chapter. Another thing to remember when working with text...
Natural Language Processing and Computational Linguistics
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Natural Language Processing and Computational Linguistics
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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 (17 chapters)
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
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