The Resource Text Analytics with Python : A Practitioner's Guide to Natural Language Processing, by Dipanjan Sarkar, (electronic resource)

Text Analytics with Python : A Practitioner's Guide to Natural Language Processing, by Dipanjan Sarkar, (electronic resource)

Label
Text Analytics with Python : A Practitioner's Guide to Natural Language Processing
Title
Text Analytics with Python
Title remainder
A Practitioner's Guide to Natural Language Processing
Statement of responsibility
by Dipanjan Sarkar
Creator
Contributor
Author
Author
Provider
Subject
Language
eng
Summary
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points • Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP • Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP • Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis
http://library.link/vocab/creatorName
Sarkar, Dipanjan
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
D8XMEFgmn1g
Image bit depth
0
LC call number
Q334-342
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
SpringerLink
http://library.link/vocab/subjectName
  • Artificial intelligence
  • Python (Computer program language)
  • Big data
  • Artificial Intelligence
  • Python
  • Big Data
Label
Text Analytics with Python : A Practitioner's Guide to Natural Language Processing, by Dipanjan Sarkar, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Chapter 1: Natural Language Processing Basics -- Chapter 2: Python for Natural Language Processing -- Chapter 3: Processing and Understanding Text -- Chapter 4: Feature Engineering for Text Data -- Chapter 5: Text Classification -- Chapter 6: Text summarization and topic modeling -- Chapter 7: Text Clustering and Similarity analysis -- Chapter 8: Sentiment Analysis -- Chapter 9: Deep learning in NLP
Dimensions
unknown
Edition
2nd ed.
Extent
XXIV, 674 p. 189 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9781484243541
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-1-4842-4354-1
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-1-4842-4354-1
Label
Text Analytics with Python : A Practitioner's Guide to Natural Language Processing, by Dipanjan Sarkar, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Chapter 1: Natural Language Processing Basics -- Chapter 2: Python for Natural Language Processing -- Chapter 3: Processing and Understanding Text -- Chapter 4: Feature Engineering for Text Data -- Chapter 5: Text Classification -- Chapter 6: Text summarization and topic modeling -- Chapter 7: Text Clustering and Similarity analysis -- Chapter 8: Sentiment Analysis -- Chapter 9: Deep learning in NLP
Dimensions
unknown
Edition
2nd ed.
Extent
XXIV, 674 p. 189 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9781484243541
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-1-4842-4354-1
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-1-4842-4354-1

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