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Python Natural Language Processing Cookbook

Name: Python Natural Language Processing Cookbook
Author: Zhenya Antić
ISBN: 9781838987312

by Zhenya Antić

March 2021

Beginner to intermediate

284 pages

English

Packt Publishing

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Python Natural Language Processing Cookbook
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedSectionsGetting readyHow to do it…How it works…There's more…See alsoGet in touchReviews
Chapter 1: Learning NLP Basics
Technical requirementsDividing text into sentencesGetting readyHow to do it…How it works…There's more…See alsoDividing sentences into words – tokenizationGetting readyHow to do it…How it works…There's more…See alsoParts of speech taggingGetting readyHow to do it…How it works…There's more…See alsoWord stemmingGetting readyHow to do it…How it works…There's more…See alsoCombining similar words – lemmatizationGetting readyHow to do it…How it works…There's more…Removing stopwordsGetting ready…How to do it…How it works…There's more…
Chapter 2: Playing with Grammar
Technical requirementsCounting nouns – plural and singular nounsGetting readyHow to do it…How it works…There's more…Getting the dependency parseGetting readyHow to do it…How it works…See alsoSplitting sentences into clausesGetting readyHow to do it…How it works…Extracting noun chunksGetting readyHow to do it…How it works…There's more…See alsoExtracting entities and relationsGetting readyHow to do it…How it works…There's more…Extracting subjects and objects of the sentenceGetting readyHow to do it…How it works…There's more…Finding references – anaphora resolutionGetting readyHow to do it…How it works…There's more…
Chapter 3: Representing Text – Capturing Semantics
Technical requirementsPutting documents into a bag of wordsGetting readyHow to do it…How it works…There's more…Constructing the N-gram modelGetting readyHow to do it…How it works…There's more…Representing texts with TF-IDFGetting readyHow to do it…How it works…There's more…Using word embeddingsGetting readyHow to do it…How it works…There's more…See alsoTraining your own embeddings modelGetting readyHow to do it…How it works…There's more…See alsoRepresenting phrases – phrase2vecGetting readyHow to do it…How it works…See alsoUsing BERT instead of word embeddingsGetting readyHow to do it…How it works…Getting started with semantic searchGetting readyHow to do it…How it works…See also
Chapter 4: Classifying Texts
Technical requirementsGetting the dataset and evaluation baseline readyGetting readyHow to do it…How it works…Performing rule-based text classification using keywordsGetting readyHow to do it…How it works…There's more…Clustering sentences using K-means – unsupervised text classificationGetting readyHow to do it…How it works…Using SVMs for supervised text classificationGetting readyHow to do it…How it works…There's more…Using LSTMs for supervised text classificationGetting readyHow to do it…How it works…
Chapter 5: Getting Started with Information Extraction
Technical requirementsUsing regular expressionsGetting readyHow to do it…How it works…There's more…Finding similar strings: the Levenshtein distanceGetting readyHow to do it…How it works…There's more…See alsoPerforming named entity recognition using spaCyGetting readyHow to do it…How it works…There's more…Training your own NER model with spaCyGetting readyHow to do it…How it works…There's more…See alsoDiscovering sentiment analysisGetting readyHow to do it…How it works…Sentiment for short texts using LSTM: TwitterGetting readyHow to do it…How it works…Using BERT for sentiment analysisGetting readyHow to do it…How it works…There's more…See also
Chapter 6: Topic Modeling
Technical requirementsLDA topic modeling with sklearnGetting readyHow to do it…How it works…There's more…LDA topic modeling with gensimGetting readyHow to do it…How it works…There's more…NMF topic modelingGetting readyHow to do it…How it works…K-means topic modeling with BERTGetting readyHow to do it…How it works…Topic modeling of short textsGetting readyHow to do it…How it works…See also
Chapter 7: Building Chatbots
Technical requirementsBuilding a basic chatbot with keyword matchingGetting readyHow to do it…How it works…There's more…Building a basic Rasa chatbotGetting readyHow to do it…How it works…There's more…See alsoCreating question-answer pairs with RasaGetting readyHow to do it…How it works…Creating and visualizing conversation paths with RasaGetting readyHow to do it…How it works…Creating actions for the Rasa chatbotGetting readyHow to do it…How it works…See also
Chapter 8: Visualizing Text Data
Technical requirementsVisualizing the dependency parseGetting readyHow to do it…How it works…Visualizing parts of speechGetting readyHow to do it…How it works…Visualizing NERGetting readyHow to do it…How it works…Constructing word cloudsGetting readyHow to do it…How it works…There's more…See alsoVisualizing topicsGetting readyHow to do it…How it works…See also

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Content preview from Python Natural Language Processing Cookbook

Chapter 3: Representing Text – Capturing Semantics

Representing the meaning of words, phrases, and sentences in a form that's understandable to computers is one of the pillars of NLP processing. Machine learning, for example, represents each data point as a fixed-size vector, and we are faced with the question of how to turn words and sentences into vectors. Almost any NLP task starts with representing the text in some numeric form, and this chapter will show several ways of doing that. Once you've learned how to represent text as vectors, you will be able to perform tasks such as classification, which will be described in later chapters.

We will also learn how to turn phrases such as fried chicken into vectors, how to train a word2vec model, ...

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Python Natural Language Processing Cookbook

by Zhenya Antić

Chapter 3: Representing Text – Capturing Semantics

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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