Python Natural Language Processing Cookbook: Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks.

Antić, Zhenya.

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Title:	Python Natural Language Processing Cookbook: Over 50 Recipes to Understand, Analyze, and Generate Text for Implementing Language Processing Tasks.
Creators:	Antić Zhenya.
Imprint:	Birmingham: Packt Publishing, Limited, 2021
Collection:	Электронные книги зарубежных издательств; Общая коллекция
Subjects:	Natural language processing (Computer science); Python (Computer program language); Natural Language Processing; Traitement automatique des langues naturelles.; Python (Langage de programmation); EBSCO eBooks
Document type:	Other
File type:	PDF
Language:	English
Rights:	Доступ по паролю из сети Интернет (чтение, печать, копирование)
Record key:	on1243534991

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Leverage your natural language processing skills to make sense of text. With this book, you'll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You'll also find recipes for overcoming common challenges in implementing NLP pipelines.

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Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Chapter 1: Learning NLP Basics
- Technical requirements
- Dividing text into sentences
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Dividing sentences into words – tokenization
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Parts of speech tagging
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Word stemming
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Combining similar words – lemmatization
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Removing stopwords
  - Getting ready…
  - How to do it…
  - How it works…
  - There's more…
Chapter 2: Playing with Grammar
- Technical requirements
- Counting nouns – plural and singular nouns
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Getting the dependency parse
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Splitting sentences into clauses
  - Getting ready
  - How to do it…
  - How it works…
- Extracting noun chunks
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Extracting entities and relations
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Extracting subjects and objects of the sentence
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Finding references – anaphora resolution
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
Chapter 3: Representing Text – Capturing Semantics
- Technical requirements
- Putting documents into a bag of words
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Constructing the N-gram model
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Representing texts with TF-IDF
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Using word embeddings
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Training your own embeddings model
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Representing phrases – phrase2vec
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Using BERT instead of word embeddings
  - Getting ready
  - How to do it…
  - How it works…
- Getting started with semantic search
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 4: Classifying Texts
- Technical requirements
- Getting the dataset and evaluation baseline ready
  - Getting ready
  - How to do it…
  - How it works…
- Performing rule-based text classification using keywords
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Clustering sentences using K-means – unsupervised text classification
  - Getting ready
  - How to do it…
  - How it works…
- Using SVMs for supervised text classification
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Using LSTMs for supervised text classification
  - Getting ready
  - How to do it…
  - How it works…
Chapter 5: Getting Started with Information Extraction
- Technical requirements
- Using regular expressions
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Finding similar strings: the Levenshtein distance
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Performing named entity recognition using spaCy
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Training your own NER model with spaCy
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Discovering sentiment analysis
  - Getting ready
  - How to do it…
  - How it works…
- Sentiment for short texts using LSTM: Twitter
  - Getting ready
  - How to do it…
  - How it works…
- Using BERT for sentiment analysis
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
Chapter 6: Topic Modeling
- Technical requirements
- LDA topic modeling with sklearn
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- LDA topic modeling with gensim
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- NMF topic modeling
  - Getting ready
  - How to do it…
  - How it works…
- K-means topic modeling with BERT
  - Getting ready
  - How to do it…
  - How it works…
- Topic modeling of short texts
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 7: Building Chatbots
- Technical requirements
- Building a basic chatbot with keyword matching
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
- Building a basic Rasa chatbot
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Creating question-answer pairs with Rasa
  - Getting ready
  - How to do it…
  - How it works…
- Creating and visualizing conversation paths with Rasa
  - Getting ready
  - How to do it…
  - How it works…
- Creating actions for the Rasa chatbot
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 8: Visualizing Text Data
- Technical requirements
- Visualizing the dependency parse
  - Getting ready
  - How to do it…
  - How it works…
- Visualizing parts of speech
  - Getting ready
  - How to do it…
  - How it works…
- Visualizing NER
  - Getting ready
  - How to do it…
  - How it works…
- Constructing word clouds
  - Getting ready
  - How to do it…
  - How it works…
  - There's more…
  - See also
- Visualizing topics
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Why subscribe?
Other Books You May Enjoy
Index

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