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Building an NLP Pipeline with spaCy for Token Classification

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Overview

Kickstart your journey into token classification by setting up an efficient NLP pipeline, learning about tokenization, POS tagging, and lemmatization with spaCy.

Syllabus

  • Unit 1: Exploring Natural Language Processing Foundations with the Reuters Corpus
    • Counting Unique Categories in Reuters Dataset
    • Explore 'Tea' Category in Reuters Corpus
    • Fetch Text and Categories for 'Coffee' in Reuters Corpus
    • Exploring the 'Gas' Category in Reuters Corpus
    • Exploring Reuters Corpus by Category
  • Unit 2: Installing and Getting Started with spaCy for NLP
    • Changing the String for Tokenization
    • Tokenize Sentences with Missing Code
    • Tokenizing First Reuters Document with spaCy
    • Calculating Unique Tokens in Document
    • Tokenizing Multiple Reuters Documents with spaCy
  • Unit 3: Mastering Advanced Tokenization Techniques in NLP with spaCy
    • Filter Non-Alphabetic Stopword Tokens
    • Identifying Out-of-Vocabulary and Digital Tokens
    • Counting Stop Word Tokens
    • Identifying Token Capitalization in Text
    • Filtering Tokens Using a Simple Pipeline
  • Unit 4: Lemmatization Nuances in Natural Language Processing with spaCy
    • Change the Sentence for Lemmatization
    • Lemmatizing Reuters Dataset with spaCy
    • Lemmatization on Reuters Dataset with spaCy
    • Integrating Lemmatization into Text Processing Pipeline
    • Lemmatization with spaCy on the Reuters Dataset
  • Unit 5: Understanding and Implementing POS Tagging with spaCy
    • Refining Output Format of POS Tagging
    • POS tagging on a Real-world Text Document
    • Analyzing Verb Usage in Reuters News
    • Frequency Analysis on Adjectives Using POS Tagging
    • Exploring Word Usages with POS Tagging

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