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Linguistics for Token Classification in spaCy

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Overview

Delve deeper into fully enhancing Token Classification by understanding linguistic and semantic aspects of Natural Language Processing. Gain grasp of language morphology and recognize entity types using spaCy.

Syllabus

  • Unit 1: Exploring Syntactic Dependencies and Token Shapes in NLP
    • Filtering Syntactic Dependencies and Token Shapes
    • Filtering Specific Syntactic Dependencies and Token Shapes
    • Creating Sentence with Unique Dependency and Shape
    • Syntactic Dependencies and Token Shapes Filtering
    • Filtering Syntactic Dependencies and Numerically Initiated Token Shapes
  • Unit 2: Understanding Semantic Similarity in NLP with spaCy
    • Semantic Similarity with Custom Sentences
    • Semantic Similarity Between Two Specific Sentences
    • Semantic Similarity Between Unrelated Sentences
    • Finding the Most Dissimilar Sentences
  • Unit 3: Recognizing Language Morphology for Advanced Token Classification in NLP Using spaCy
    • Extracting Specific Morphological Features for Verbs
    • Extract Number Feature from Noun Tokens
    • Create a Sentence with Specific Morphological Features
    • Discovering Feature-Rich Sentence in Text Analysis
  • Unit 4: Unveiling the Essentials of Entity Recognition with spaCy
    • Filtering Out Organization Entities
    • Identifying Specific Entities in Custom Text
    • Extracting 'ORG' and 'GPE' Entities with Spacy
    • Unique Geopolitical Entities in Reuters Dataset
  • Unit 5: Expanding the spaCy NLP Pipeline with Custom Components
    • Modify Phonetic Key Function in spaCy
    • Implement Verb Count Pipeline Component
    • Creating a Vowel Detection Custom Extension in spaCy
    • Implement Same POS Counting Pipeline Component

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