Getting started with natural language processing

Getting started with natural language processing

Getting Started with Natural Language Processing is an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python c...

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Detalles Bibliográficos
Otros Autores: Kochmar, Ekaterina, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Shelter Island, New York : Manning Publications Company [2022]
Materias:
Natural language processing (Computer science)
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009689937406719
Tabla de Contenidos:
  • Intro
  • inside front cover
  • Getting Started with Natural Language Processing
  • Copyright
  • dedication
  • contents
  • front matter
  • preface
  • acknowledgments
  • about this book
  • Who should read this book
  • How this book is organized: A road map
  • About the code
  • liveBook discussion forum
  • Other online resources
  • about the author
  • about the cover illustration
  • 1 Introduction
  • 1.1 A brief history of NLP
  • 1.2 Typical tasks
  • 1.2.1 Information search
  • 1.2.2 Advanced information search: Asking the machine precise questions
  • 1.2.3 Conversational agents and intelligent virtual assistants
  • 1.2.4 Text prediction and language generation
  • 1.2.5 Spam filtering
  • 1.2.6 Machine translation
  • 1.2.7 Spell- and grammar checking
  • Summary
  • Solution to exercise 1.1
  • 2 Your first NLP example
  • 2.1 Introducing NLP in practice: Spam filtering
  • 2.2 Understanding the task
  • 2.2.1 Step 1: Define the data and classes
  • 2.2.2 Step 2: Split the text into words
  • 2.2.3 Step 3: Extract and normalize the features
  • 2.2.4 Step 4: Train a classifier
  • 2.2.5 Step 5: Evaluate the classifier
  • 2.3 Implementing your own spam filter
  • 2.3.1 Step 1: Define the data and classes
  • 2.3.2 Step 2: Split the text into words
  • 2.3.3 Step 3: Extract and normalize the features
  • 2.3.4 Step 4: Train the classifier
  • 2.3.5 Step 5: Evaluate your classifier
  • 2.4 Deploying your spam filter in practice
  • Summary
  • Solutions to miscellaneous exercises
  • 3 Introduction to information search
  • 3.1 Understanding the task
  • 3.1.1 Data and data structures
  • 3.1.2 Boolean search algorithm
  • 3.2 Processing the data further
  • 3.2.1 Preselecting the words that matter: Stopwords removal
  • 3.2.2 Matching forms of the same word: Morphological processing
  • 3.3 Information weighing
  • 3.3.1 Weighing words with term frequency.
  • 3.3.2 Weighing words with inverse document frequency
  • 3.4 Practical use of the search algorithm
  • 3.4.1 Retrieval of the most similar documents
  • 3.4.2 Evaluation of the results
  • 3.4.3 Deploying search algorithm in practice
  • Summary
  • Solutions to miscellaneous exercises
  • 4 Information extraction
  • 4.1 Use cases
  • 4.1.1 Case 1
  • 4.1.2 Case 2
  • 4.1.3 Case 3
  • 4.2 Understanding the task
  • 4.3 Detecting word types with part-of-speech tagging
  • 4.3.1 Understanding word types
  • 4.3.2 Part-of-speech tagging with spaCy
  • 4.4 Understanding sentence structure with syntactic parsing
  • 4.4.1 Why sentence structure is important
  • 4.4.2 Dependency parsing with spaCy
  • 4.5 Building your own information extraction algorithm
  • Summary
  • Solutions to miscellaneous exercises
  • 5 Author profiling as a machine-learning task
  • 5.1 Understanding the task
  • 5.1.1 Case 1: Authorship attribution
  • 5.1.2 Case 2: User profiling
  • 5.2 Machine-learning pipeline at first glance
  • 5.2.1 Original data
  • 5.2.2 Testing generalization behavior
  • 5.2.3 Setting up the benchmark
  • 5.3 A closer look at the machine-learning pipeline
  • 5.3.1 Decision Trees classifier basics
  • 5.3.2 Evaluating which tree is better using node impurity
  • 5.3.3 Selection of the best split in Decision Trees
  • 5.3.4 Decision Trees on language data
  • Summary
  • Solutions to miscellaneous exercises
  • 6 Linguistic feature engineering for author profiling
  • 6.1 Another close look at the machine-learning pipeline
  • 6.1.1 Evaluating the performance of your classifier
  • 6.1.2 Further evaluation measures
  • 6.2 Feature engineering for authorship attribution
  • 6.2.1 Word and sentence length statistics as features
  • 6.2.2 Counts of stopwords and proportion of stopwords as features
  • 6.2.3 Distributions of parts of speech as features.
  • 6.2.4 Distribution of word suffixes as features
  • 6.2.5 Unique words as features
  • 6.3 Practical use of authorship attribution and user profiling
  • Summary
  • 7 Your first sentiment analyzer using sentiment lexicons
  • 7.1 Use cases
  • 7.2 Understanding your task
  • 7.2.1 Aggregating sentiment score with the help of a lexicon
  • 7.2.2 Learning to detect sentiment in a data-driven way
  • 7.3 Setting up the pipeline: Data loading and analysis
  • 7.3.1 Data loading and preprocessing
  • 7.3.2 A closer look into the data
  • 7.4 Aggregating sentiment scores with a sentiment lexicon
  • 7.4.1 Collecting sentiment scores from a lexicon
  • 7.4.2 Applying sentiment scores to detect review polarity
  • Summary
  • Solutions to exercises
  • 8 Sentiment analysis with a data-driven approach
  • 8.1 Addressing multiple senses of a word with SentiWordNet
  • 8.2 Addressing dependence on context with machine learning
  • 8.2.1 Data preparation
  • 8.2.2 Extracting features from text
  • 8.2.3 Scikit-learn's machine-learning pipeline
  • 8.2.4 Full-scale evaluation with cross-validation
  • 8.3 Varying the length of the sentiment-bearing features
  • 8.4 Negation handling for sentiment analysis
  • 8.5 Further practice
  • Summary
  • 9 Topic analysis
  • 9.1 Topic classification as a supervised machine-learning task
  • 9.1.1 Data
  • 9.1.2 Topic classification with Naïve Bayes
  • 9.1.3 Evaluation of the results
  • 9.2 Topic discovery as an unsupervised machine-learning task
  • 9.2.1 Unsupervised ML approaches
  • 9.2.2 Clustering for topic discovery
  • 9.2.3 Evaluation of the topic clustering algorithm
  • Summary
  • Solutions to miscellaneous exercises
  • 10 Topic modeling
  • 10.1 Topic modeling with latent Dirichlet allocation
  • 10.1.1 Exercise 10.1: Question 1 solution
  • 10.1.2 Exercise 10.1: Question 2 solution
  • 10.1.3 Estimating parameters for the LDA.
  • 10.1.4 LDA as a generative model
  • 10.2 Implementation of the topic modeling algorithm
  • 10.2.1 Loading the data
  • 10.2.2 Preprocessing the data
  • 10.2.3 Applying the LDA model
  • 10.2.4 Exploring the results
  • Summary
  • Solutions to miscellaneous exercises
  • 11 Named-entity recognition
  • 11.1 Named entity recognition: Definitions and challenges
  • 11.1.1 Named entity types
  • 11.1.2 Challenges in named entity recognition
  • 11.2 Named-entity recognition as a sequence labeling task
  • 11.2.1 The basics: BIO scheme
  • 11.2.2 What does it mean for a task to be sequential?
  • 11.2.3 Sequential solution for NER
  • 11.3 Practical applications of NER
  • 11.3.1 Data loading and exploration
  • 11.3.2 Named entity types exploration with spaCy
  • 11.3.3 Information extraction revisited
  • 11.3.4 Named entities visualization
  • Summary
  • Conclusion
  • Solutions to miscellaneous exercises
  • Appendix A Installation instructions
  • index
  • inside back cover.

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