Natural Language Processing Basics Syllabus

Course Description:

This course provides an introduction to the fundamental concepts and techniques of natural language processing (NLP). Students will learn about linguistic structure, text preprocessing, and common NLP tasks such as text classification and sentiment analysis.

Week 1: Introduction to Natural Language Processing

• Overview of NLP
• Applications of NLP
• Challenges in NLP

Week 2: Linguistic Structure

• Basic linguistic concepts (e.g., morphology, syntax, semantics)
• Tokenization and stemming
• Part-of-speech tagging

Week 3: Text Preprocessing

• Text normalization (e.g., lowercasing, removing stopwords)
• Named entity recognition
• Text segmentation

Week 4: Text Representation

• Bag-of-words model
• TF-IDF representation
• Word embeddings (e.g., Word2Vec, GloVe)

Week 5: NLP Tasks

• Text classification
• Sentiment analysis
• Named entity recognition
• Text summarization

Week 6: Language Models and Grammar

• N-gram models
• Syntax and grammar in NLP
• Context-free grammars

Week 7: Advanced Topics

• Topic modeling (e.g., LDA)
• Neural networks for NLP
• Machine translation
• Question answering systems

Week 8: Final Project

• Students work on a project applying NLP techniques to a real-world dataset

Assessment:

• Weekly quizzes or assignments
• Final project presentation and report

Prerequisites:

Basic knowledge of programming (Python) and familiarity with basic machine learning concepts are recommended but not required.