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Over 80 practical recipes on natural language processing techniques using Python’s NLTK 3.0About This BookBreak text down into its component parts for spelling correction, feature extraction, and phrase transformationLearn how to do custom sentiment analysis and named entity recognitionWork through the natural language processing concepts with simple and easy-to-follow programming recipesIn DetailThis 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.What You Will LearnTokenize text into sentences, and sentences into wordsLook up words in the WordNet dictionaryApply spelling correction and word replacementAccess the built-in text corpora and create your own custom corpusTag words with parts of speechChunk phrases and recognize named entitiesGrammatically transform phrases and chunksClassify text and perform sentiment analysis
