Semantics & Meaning: Understanding Language in NLP

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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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