Syntax and Structure: Foundations of Language Understanding in NLP
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Course Modules:
Module 1: What is Syntax in NLP?
Definitions: syntax vs. semantics
Why syntactic structure matters in AI
Examples of syntactic ambiguity and errors
Module 2: Part-of-Speech (POS) Tagging
Introduction to lexical categories (noun, verb, adjective, etc.)
Rule-based vs. statistical POS taggers
Using NLTK, spaCy, and Stanza for POS tagging
Module 3: Phrase Structure Grammar
Sentence constituents: noun phrases, verb phrases, etc.
Context-free grammar and tree structures
Parsing sentences into phrase trees
Module 4: Dependency Parsing
Dependency grammar vs. phrase structure
Understanding head-dependent relationships
Visualizing sentence structures with dependency graphs
Module 5: Syntax-Based NLP Applications
How syntax improves machine translation, chatbots, and text classification
Role of syntax in text generation and summarization
Syntax-aware embeddings and transformers
Module 6: Capstone Project – Syntax Analysis Pipeline
Choose a dataset (e.g., news articles, tweets, or essays)
Implement POS tagging and dependency parsing
Submit annotated outputs and visual diagrams
Tools & Technologies Used:
Python (NLTK, spaCy, Stanza)
Constituency and dependency parsers
Tree visualizers and syntax plot tools
Jupyter Notebook / Google Colab
Target Audience:
NLP learners and data scientists
Linguists exploring computational language tools
Developers working on grammar-aware applications
Students in linguistics, AI, or language technology
Global Learning Benefits:
Understand sentence grammar for advanced text analysis
Apply syntactic parsing to improve NLP model accuracy
Visualize and interpret language like a machine does
Bridge linguistic knowledge with AI implementation
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
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