Semantics & Meaning: Understanding Language in NLP
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Semantics in NLP
What is semantics, and how is it different from syntax?
Why understanding meaning is hard for machines
Key tasks: disambiguation, sentiment, entailment
Module 2: Lexical Semantics & Word Relationships
Polysemy, synonymy, antonymy, hypernymy
WordNet and semantic networks
Challenges of ambiguous meaning
Module 3: Vector Space Models & Word Embeddings
Representing words as vectors
One-hot encoding vs. Word2Vec, GloVe
Measuring semantic similarity in vector space
Module 4: Contextual Embeddings and Transformers
Limitations of static embeddings
BERT, RoBERTa, and contextual meaning
Sentence embeddings and cross-sentence understanding
Module 5: Semantic Analysis Applications
Semantic search and question answering
Text summarization and translation
Natural language inference and dialogue systems
Module 6: Capstone Project – Semantic Similarity Tool
Choose a task: sentiment detector, question matcher, or paraphrase checker
Use pre-trained models to compute semantic similarity
Submit notebook, analysis, and user interface (if applicable)
Tools & Technologies Used:
Python, NLTK, spaCy, Gensim
Hugging Face Transformers (BERT, DistilBERT)
WordNet, Sentence Transformers
Google Colab / Jupyter Notebooks
Target Audience:
AI and NLP learners
Data scientists building semantic-aware models
Developers creating intelligent assistants and search tools
Linguists exploring meaning in computational systems
Global Learning Benefits:
Understand how AI models interpret language meaning
Learn to apply embeddings and semantic techniques in real projects
Improve accuracy in meaning-based NLP tasks
Gain tools to build smarter, more human-like language systems
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