Python 3 Text Processing with NLTK 3 Cookbook

Häftad, Engelska, 2014

637 kr

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Beskrivning

Key FeaturesBook DescriptionThis book is intended for Python programmers interested in learning how to do natural language processing. Maybe you’ve learned the limits of regular expressions the hard way, or you’ve realized that human language cannot be deterministically parsed like a computer language. Perhaps you have more text than you know what to do with, and need automated ways to analyze and structure that text. This Cookbook will show you how to train and use statistical language models to process text in ways that are practically impossible with standard programming tools. A basic knowledge of Python and the basic text processing concepts is expected. Some experience with regular expressions will also be helpful.What you will learnTokenize text into sentences, and sentences into wordsLook up words in the WordNet dictionaryApply spelling correction and word replacementAccess the builtin 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 analysisWho this book is for

Produktinformation

Utgivningsdatum:2014-08-26
Mått:191 x 235 x 17 mm
Vikt:572 g
Format:Häftad
Språk:Engelska
Antal sidor:304
Upplaga:2
Förlag:Packt Publishing Limited
ISBN:9781782167853

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

Jacob Perkins is the cofounder and CTO of Weotta, a local search company. Weotta uses NLP and machine learning to create powerful and easy-to-use natural language search for what to do and where to go. He is the author of Python Text Processing with NLTK 2.0 Cookbook, Packt Publishing, and has contributed a chapter to the Bad Data Handbook, O'Reilly Media. He writes about NLTK, Python, and other technology topics at http://streamhacker.com. To demonstrate the capabilities of NLTK and natural language processing, he developed http://text-processing.com, which provides simple demos and NLP APIs for commercial use. He has contributed to various open source projects, including NLTK, and created NLTK-Trainer to simplify the process of training NLTK models. For more information, visit https://github.com/japerk/nltk-trainer.

Innehållsförteckning

Table of ContentsTokenizing Text and WordNet BasicsReplacing and Correcting WordsCreating Custom CorporaTagging Part of SpeechExtraction Chunks: Partial ParsingTransforming and Normalizing ChunksExtracting Features for Classifying Words and ChunksDistributed Processing and Handling Large DatasetsParsing and Matching Specific DataAppendix 1