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

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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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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
Index
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