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Natural Language Processing and Computational Linguistics

Natural Language Processing and Computational Linguistics

By : Bhargav Srinivasa-Desikan
3.6 (7)
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Natural Language Processing and Computational Linguistics

Natural Language Processing and Computational Linguistics

3.6 (7)
By: Bhargav Srinivasa-Desikan

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)
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Generating text

In our discussions involving deep learning and natural language processing, we extensively spoke about how it is used in text generation to very convincing results we are now going to get our hands dirty with a little bit of text generation ourselves.

The neural network architecture we will be using is a recurrent neural network, and in particular, an LSTM [9]. LSTM stands for Long Short Term Memory and is unique because its architecture allows it to capture both short term and long term context of words in a sentence. The very popular blog post Understanding LSTM Networks [11] by deep learning researcher Colah is a great way to further understand LSTMs.

This is the same architecture used in the popular blog post [10] by Andrej Karpathy, The unreasonable effectiveness of Neural Networks, though Karpathy wrote his code for his NN in Lua we will...

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