عنوان
پدید آورنده
موضوع
رده
QA76.9.N38 .S656 2018eb
QA76
.
9
.
N38
.
S656
2018eb
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
1788837037
(Number (ISBN
178883853X
(Number (ISBN
9781788837033
(Number (ISBN
9781788838535
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Natural Language Processing and Computational Linguistics :
General Material Designation
[Book]
Other Title Information
a Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Birmingham :
Name of Publisher, Distributor, etc.
Packt Publishing Ltd,
Date of Publication, Distribution, etc.
2018.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (298 pages)
CONTENTS NOTE
Text of Note
Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: What is Text Analysis?; What is text analysis?; Where's the data at?; Garbage in, garbage out; Why should you do text analysis?; Summary; References; Chapter 2: Python Tips for Text Analysis; Why Python?; Text manipulation in Python; Summary; References; Chapter 3: spaCy's Language Models; spaCy; Installation; Troubleshooting; Language models; Installing language models; Installation -- how and why?; Basic preprocessing with language models; Tokenizing text; Part-of-speech (POS) -- tagging.
Text of Note
Chapter 13: Deep Learning for TextDeep learning; Deep learning for text (and more); Generating text; Summary; References; Chapter 14: Keras and spaCy for Deep Learning; Keras and spaCy; Classification with Keras; Classification with spaCy; Summary; References; Chapter 15: Sentiment Analysis and ChatBots; Sentiment analysis; Reddit for mining data; Twitter for mining data; ChatBots; Summary; References; Other Books You May Enjoy; Index.
Text of Note
Exploring documentsTopic coherence and evaluating topic models; Visualizing topic models; Summary; References; Chapter 10: Clustering and Classifying Text; Clustering text; Starting clustering; K-means; Hierarchical clustering; Classifying text; Summary; References; Chapter 11: Similarity Queries and Summarization; Similarity metrics; Similarity queries; Summarizing text; Summary; References; Chapter 12: Word2Vec, Doc2Vec, and Gensim; Word2Vec; Using Word2Vec with Gensim; Doc2Vec; Other word embeddings; GloVe; FastText; WordRank; Varembed; Poincare; Summary; References.
Text of Note
Named entity recognitionRule-based matching; Preprocessing; Summary; References; Chapter 4: Gensim -- Vectorizing Text and Transformations and n-grams; Introducing Gensim; Vectors and why we need them; Bag-of-words; TF-IDF; Other representations; Vector transformations in Gensim; n-grams and some more preprocessing; Summary; References; Chapter 5: POS-Tagging and Its Applications; What is POS-tagging?; POS-tagging in Python; POS-tagging with spaCy; Training our own POS-taggers; POS-tagging code examples; Summary; References; Chapter 6: NER-Tagging and Its Applications; What is NER-tagging?
Text of Note
NER-tagging in PythonNER-tagging with spaCy; Training our own NER-taggers; NER-tagging examples and visualization; Summary; References; Chapter 7: Dependency Parsing; Dependency parsing; Dependency parsing in Python; Dependency parsing with spaCy; Training our dependency parsers; Summary; References; Chapter 8: Topic Models; What are topic models?; Topic models in Gensim; Latent Dirichlet allocation; Latent semantic indexing; Hierarchical Dirichlet process; Dynamic topic models; Topic models in scikit-learn; Summary; References; Chapter 9: Advanced Topic Modeling; Advanced training tips.
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SUMMARY OR ABSTRACT
Text of Note
Discover how you can perform your own modern text analysis, to make predictions, create inferences, and gain insights about the data around you today. Learn how to harness the powerful Python ecosystem and tools such as spaCy and Gensim to perform natural language processing, and computational linguistics algorithms.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
01201872
Stock Number
B09470
OTHER EDITION IN ANOTHER MEDIUM
Title
Natural Language Processing and Computational Linguistics : A Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras.
International Standard Book Number
9781788838535
TOPICAL NAME USED AS SUBJECT
Computational linguistics.
Machine learning.
Natural language processing (Computer science)
Python (Computer program language)
Artificial intelligence.
Computational linguistics.
Computers-- Intelligence (AI) & Semantics.
Computers-- Natural Language Processing.
Computers-- Neural Networks.
Machine learning.
Natural language & machine translation.
Natural language processing (Computer science)
Neural networks & fuzzy systems.
Python (Computer program language)
DEWEY DECIMAL CLASSIFICATION
Number
006
.
35
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
9
.
N38
Book number
.
S656
2018eb
PERSONAL NAME - PRIMARY RESPONSIBILITY
Srinivasa-Desikan, Bhargav.
ORIGINATING SOURCE
Date of Transaction
20200823060505.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
