Python Natural Language Processing Cookbook Over 60 Recipes for Building Powerful NLP Solutions Using Python and LLM Libraries
Harness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advanceme...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2024]
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009850431106719 |
Tabla de Contenidos:
- Cover
- Title page
- Copyright and credits
- Dedication
- Contributors
- Table of Contents
- Preface
- Chapter 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…
- 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
- Getting ready
- How to do it…
- There's more…
- Chapter 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
- Chapter 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…
- Chapter 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…
- Chapter 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…
- Chapter 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
- Chapter 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
- Chapter 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...
- Chapter 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
- Chapter 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
- Other Books You May Enjoy.
