Python Natural Language Processing Cookbook: Over 60 recipes for building powerful NLP solutions using Python, 2nd Edition

Python Natural Language Processing Cookbook
Contributors
About the authors
About the reviewer
Preface
   Who this book is for
   What this book covers
      To get the most out of this book
   Download the example code files
   Conventions used
   Get in touch
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   Download a free PDF copy of this book
1
Learning NLP Basics
   Technical requirements
   Dividing text into sentences
      Getting ready
      How to do it…
      There’s more…
      See also
   Dividing sentences into words – tokenization
      Getting ready
      How to do it
      There’s more…
      There\'s still more
      See also
   Part of speech tagging
      Getting ready
      How to do it…
      There’s more
      There’s more
      See also
   Combining similar words – lemmatization
      Getting ready
      How to do it…
      There’s more…
   Removing stopwords
      How to do it…
      There’s more…
      There\'s still more
2
Playing with Grammar
   Technical requirements
   Counting nouns – plural and singular nouns
      Getting ready
      How to do it…
      There’s more…
   Getting the dependency parse
      Getting ready
      How to do it…
      See also
   Extracting noun chunks
      Getting ready
      How to do it…
      There’s more…
      See also
   Extracting subjects and objects of the sentence
      Getting ready
      How to do it…
      There’s more…
   Finding patterns in text using grammatical information
      Getting ready
      How to do it…
      See also
3
Representing Text – Capturing Semantics
   Technical requirements
   Creating a simple classifier
      Getting ready
      How to do it…
      There’s more…
   Putting documents into a bag of words
      Getting ready
      How to do it…
   Constructing an N-gram model
      Getting ready
      How to do it…
      There’s more…
   Representing texts with TF-IDF
      Getting ready
      How to do it…
      How it works…
      There’s more…
      See also
   Using word embeddings
      Getting ready
      How to do it…
      There’s more…
      See also
   Training your own embeddings model
      Getting ready
      How to do it…
      There’s more…
      See also
   Using BERT and OpenAI embeddings instead of word embeddings
      Getting ready
      How to do it…
      There’s more…
      See also
   Retrieval augmented generation (RAG)
      Getting ready
      How to do it…
4
Classifying Texts
   Technical requirements
   Getting the dataset and evaluation ready
      Getting ready
      How to do it…
   Performing rule-based text classification using keywords
      Getting ready
      How to do it…
   Clustering sentences using K-Means – unsupervised text classification
      Getting ready
      How to do it…
   Using SVMs for supervised text classification
      Getting ready
      How to do it…
      There’s more…
   Training a spaCy model for supervised text classification
      Getting ready
      How to do it…
   Classifying texts using OpenAI models
      Getting ready
      How to do it…
5
Getting Started with Information Extraction
   Technical requirements
   Using regular expressions
      Getting ready
      How to do it…
      There’s more…
   Finding similar strings – Levenshtein distance
      Getting ready
      How to do it…
      There’s more…
   Extracting keywords
      Getting ready
      How to do it…
      There’s more…
   Performing named entity recognition using spaCy
      Getting ready
      How to do it…
      There’s more…
   Training your own NER model with spaCy
      Getting ready
      How to do it…
      See also
   Fine-tuning BERT for NER
      Getting ready
      How to do it…
6
Topic Modeling
   Technical requirements
   LDA topic modeling with gensim
      Getting ready
      How to do it...
      There’s more...
   Community detection clustering with SBERT
      Getting ready
      How to do it...
   K-Means topic modeling with BERT
      Getting ready
      How to do it...
   Topic modeling using BERTopic
      Getting ready
      How to do it...
      There’s more...
   Using contextualized topic models
      Getting ready
      How to do it...
      See also
7
Visualizing Text Data
   Technical requirements
   Visualizing the dependency parse
      Getting ready
      How to do it...
   Visualizing parts of speech
      Getting ready
      How to do it...
   Visualizing NER
      Getting ready
      How to do it...
   Creating a confusion matrix plot
      Getting ready
      How to do it...
   Constructing word clouds
      Getting ready
      How to do it...
      There’s more...
      See also
   Visualizing topics from Gensim
      Getting ready
      How to do it...
      See also
   Visualizing topics from BERTopic
      Getting ready
      How to do it...
      See also
8
Transformers and Their Applications
   Technical requirements
   Loading a dataset
      Getting ready
      How to do it...
   Tokenizing the text in your dataset
      Getting ready
      How to do it...
   Classifying text
      Getting ready
      How to do it...
   Using a zero-shot classifier
      Getting ready
      How to do it...
   Generating text
      Getting ready
      How to do it...
      There’s more…
   Language translation
      Getting ready
      How to do it...
9
Natural Language Understanding
   Technical requirements
   Answering questions from a short text passage
      Getting ready
      How to do it...
   Answering questions from a long text passage
      Getting ready
      How to do it...
      See also
   Answering questions from a document corpus in an extractive manner
      Getting ready
      How to do it...
      See also
   Answering questions from a document corpus in an abstractive manner
      Getting ready
      How to do it
      See also
   Summarizing text using pre-trained models based on Transformers
      Getting ready
      How to do it
      There’s more…
      See also
   Detecting sentence entailment
      Getting ready
      How to do it...
      There’s more...
   Enhancing explainability via a classifier-invariant approach
      Getting ready
      How to do it...
      There’s more...
   Enhancing explainability via text generation
      Getting ready
      How to do it
10
Generative AI and Large Language Models
   Technical requirements
      Model access
   Running an LLM locally
      Getting ready
      How to do it…
   Running an LLM to follow instructions
      Getting ready
      How to do it…
      There’s more…
   Augmenting an LLM with external data
      Executing a simple prompt-to-LLM chain
      Augmenting the LLM with external content
   Creating a chatbot using an LLM
      Getting ready
      How to do it…
   Generating code using an LLM
      Getting ready
      How to do it…
      There’s more…
   Generating a SQL query using human-defined requirements
      Getting ready
      How to do it…
   Agents – making an LLM to reason and act
      Getting ready
      How to do it…
   Using OpenAI models instead of local ones
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