Jacob Perkins

Python 3 Text Processing with NLTK 3 Cookbook

This book will show you the essential techniques of text and language processing. Starting with tokenization, stemming, and the WordNet dictionary, you'll progress to part-of-speech tagging, phrase chunking, and named entity recognition. You'll learn how various text corpora are organized, as well as how to create your own custom corpus. Then, you'll move onto text classification with a focus on sentiment analysis. And because NLP can be computationally expensive on large bodies of text, you'll try a few methods for distributed text processing. Finally, you'll be introduced to a number of other small but complementary Python libraries for text analysis, cleaning, and parsing.
This cookbook provides simple, straightforward examples so you can quickly learn text processing with Python and NLTK.
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Packt Publishing
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  • niodeyaцитує4 роки тому
    Most of the time, the default sentence tokenizer will be sufficient
  • niodeyaцитує4 роки тому
    Once you have a custom sentence tokenizer, you can use it for your own corpora
  • niodeyaцитує4 роки тому
    The PunktSentenceTokenizer class uses an unsupervised learning algorithm to learn what constitutes a sentence break. It is unsupervised because you don't have to give it any labeled training data, just raw text

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