Treffer: Natural Language Processing with Python Quick Start Guide : Going From a Python Developer to an Effective Natural Language Processing Engineer

Weighted classifiers ; NLP in Python is among the most sought-after skills among data scientists. With code and relevant case studies, this book will show how you can use industry grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. ; Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Text Classification; What is NLP?; Why learn about NLP?; You have a problem in mind; Technical achievement; Do something new; Is this book for you?; NLP workflow template; Understanding the problem; Understanding and preparing the data; Quick wins -- proof of concept; Iterating and improving; Algorithms; Pre-processing; Evaluation and deployment; Evaluation; Deployment; Example -- text classification workflow; Launchpad -- programming environment setup ; Text classification in 30 lines of codeGetting the data; Text to numbers; Machine learning; Summary; Chapter 2: Tidying your Text; Bread and butter -- most common tasks; Loading the data; Exploring the loaded data; Tokenization; Intuitive -- split by whitespace; The hack -- splitting by word extraction; Introducing Regexes; spaCy for tokenization; How does the spaCy tokenizer work?; Sentence tokenization; Stop words removal and case change; Stemming and lemmatization; spaCy for lemmatization; -PRON-; Case-insensitive; Conversion -- meeting to meet; spaCy compared with NLTK and CoreNLP ; Correcting spellingFuzzyWuzzy; Jellyfish; Phonetic word similarity; What is a phonetic encoding?; Runtime complexity; Cleaning a corpus with FlashText; Summary; Chapter 3: Leveraging Linguistics; Linguistics and NLP; Getting started; Introducing textacy; Redacting names with named entity recognition; Entity types; Automatic question generation; Part-of-speech tagging; Creating a ruleset; Question and answer generation using dependency parsing; Visualizing the relationship; Introducing textacy; Leveling ...