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- Python 3 Text Processing with NLTK 3 Cookbook
Item #:
39123153
Python 3 Text Processing with NLTK 3 Cookbook
Item #:
39123153
IMP 57
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This cookbook provides simple, straightforward examples so you can quickly learn text processing with Python and NLTK.
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Product Details
- Over 80 practical recipes on natural language processing techniques using Python's NLTK 3.0
- Break text down into its component parts for spelling correction, feature extraction, and phrase transformation
- Learn how to do custom sentiment analysis and named entity recognition
- Tokenize text into sentences, and sentences into words
- Apply spelling correction and word replacement
- Access the built-in text corpora and create your own custom corpus
| Publisher | Packt Publishing |
| Publication date | August 26, 2014 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 310 pages |
| ISBN-10 | 1782167854 |
| ISBN-13 | 978-1782167853 |
| Item Weight | 1.15 pounds (520 grams) |
| Dimensions | 7.5 x 0.69 x 9.25 inches (19.1 x 1.8 x 23.5 cm) |
Product Description
Python 3 Text Processing with NLTK 3 Cookbook
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Natural Language Processing Editorial Review
Python 3 Text Processing with NLTK 3 Cookbook Illustrated Edition is a textbook for beginners who want to learn about NLTK. The book provides solutions, but they are not effective or thorough. Some red URLs or package mentioned in the book are outdated and not useful. The author is also unresponsive to readers' queries, which negatively impacted reader experience. Compared to the free "Natural Language Processing with Python" (2009), this book offers poor communication, content, and utility.
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Pros
- Provides an introduction for those new to NLTK
Cons
- Solutions are not effective or thorough
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Features & Benefits
- Break text down into its component parts for spelling correction, feature extraction, and phrase transformation
- Do custom sentiment analysis and named entity recognition
- Create your own custom corpus and access the built-in text corpora
- Perform text classification with a focus on sentiment analysis
- Try a few methods for distributed text processing
- Learn text processing with Python and NLTK
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