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Natural Language Processing with Python Quick Start Guide

You're reading from   Natural Language Processing with Python Quick Start Guide Going from a Python developer to an effective Natural Language Processing Engineer

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789130386
Length 182 pages
Edition 1st Edition
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Concepts
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Author (1):
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Nirant Kasliwal Nirant Kasliwal
Author Profile Icon Nirant Kasliwal
Nirant Kasliwal
LinkedIn
Nirant Kasliwal maintains an awesome list of NLP natural language processing resources. GitHub's machine learning collection features this as the go-to guide. Nobel Laureate Dr. Paul Romer found his programming notes on Jupyter Notebooks helpful. Nirant won the first ever NLP Google Kaggle Kernel Award. At Soroco, image segmentation and intent categorization are the challenges he works with. His state-of-the-art language modeling results are available as Hindi2vec.
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Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with Text Classification FREE CHAPTER 2. Tidying your Text 3. Leveraging Linguistics 4. Text Representations - Words to Numbers 5. Modern Methods for Classification 6. Deep Learning for NLP 7. Building your Own Chatbot 8. Web Deployments 9. Other Books You May Enjoy

spaCy compared with NLTK and CoreNLP

The following is a comparison of the NLTK and CoreNLP:

Feature Spacy NLTK CoreNLP
Native Python support/API Y Y Y
Multi-language support Y Y Y
Tokenization Y Y Y
Part-of-speech tagging Y Y Y
Sentence segmentation Y Y Y
Dependency parsing Y N Y
Entity recognition Y Y Y
Integrated word vectors Y N N
Sentiment analysis Y Y Y
Coreference resolution N N Y

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Authors (1)

Profile icon Kasliwal
Kasliwal
Github icon
Nirant Kasliwal maintains an awesome list of NLP natural language processing resources. GitHub's machine learning collection features this as the go-to guide. Nobel Laureate Dr. Paul Romer found his programming notes on Jupyter Notebooks helpful. Nirant won the first ever NLP Google Kaggle Kernel Award. At Soroco, image segmentation and intent categorization are the challenges he works with. His state-of-the-art language modeling results are available as Hindi2vec.
Read more
See other products by Kasliwal

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