Introduction to Natural Language Processing (NLP)

Course Title: Introduction to Natural Language Processing (NLP)

Course Description: This course introduces students to the principles, techniques, and applications of Natural Language Processing (NLP). Students will learn about NLP tools, algorithms, and models used for tasks such as text classification, sentiment analysis, named entity recognition, machine translation, and more.

Course Outline:

  1. Week 1: Introduction to NLP
    • Definition and scope of Natural Language Processing
    • Importance and applications of NLP in various fields
    • Overview of NLP tools, libraries, and resources
  2. Week 2: Text Preprocessing
    • Tokenization and word segmentation
    • Stopword removal and punctuation handling
    • Text normalization (lowercasing, stemming, lemmatization)
  3. Week 3: Part-of-Speech Tagging
    • Understanding parts of speech (nouns, verbs, adjectives, etc.)
    • POS tagging algorithms (rule-based, statistical, deep learning)
    • POS tagging applications in information extraction
  4. Week 4: Named Entity Recognition (NER)
    • Identifying named entities (persons, organizations, locations, etc.)
    • NER algorithms and models (rule-based, CRF, deep learning)
    • NER applications in text mining and knowledge extraction
  5. Week 5: Text Classification
    • Supervised learning for text classification (Naive Bayes, SVM, neural networks)
    • Feature extraction for text classification (bag-of-words, TF-IDF)
    • Text classification applications (spam detection, sentiment analysis)
  6. Week 6: Sentiment Analysis
    • Analyzing sentiment in text (positive, negative, neutral)
    • Sentiment analysis techniques (lexicon-based, machine learning)
    • Sentiment analysis applications in social media and customer reviews
  7. Week 7: Language Modeling and Machine Translation
    • Language modeling with N-grams and neural networks
    • Machine translation approaches (rule-based, statistical, neural MT)
    • Evaluation metrics for machine translation quality
  8. Week 8: Text Generation
    • Generation of text using language models (GPT, BERT)
    • Text summarization techniques (extractive, abstractive)
    • Applications of text generation in chatbots and content generation
  9. Week 9: Information Retrieval and Question Answering
    • Retrieval models for information retrieval (TF-IDF, BM25)
    • Question Answering (QA) systems (rule-based, information retrieval, deep learning)
    • QA applications in search engines and virtual assistants
  10. Week 10: Advanced Topics in NLP
    • Named entity disambiguation and linking
    • Coreference resolution and discourse analysis
    • Ethical considerations in NLP research and applications

Course Assignments:

  • Weekly readings and discussions on NLP topics
  • Text preprocessing and feature extraction tasks
  • POS tagging and NER exercises
  • Text classification and sentiment analysis projects
  • Machine translation and text generation assignments
  • Information retrieval and QA system development
  • Final project: NLP application or research project

Course Materials:

  • Textbook: “Natural Language Processing in Python” by Jacob Perkins
  • NLP libraries and tools (NLTK, spaCy, Transformers)
  • Machine learning frameworks (Scikit-learn, TensorFlow, PyTorch)
  • NLP datasets and corpora for experimentation
  • Research papers and articles on NLP advancements

Assessment:

  • Participation in class discussions and activities
  • Weekly quizzes and assignments on NLP techniques
  • NLP project milestones and progress reports
  • Final project evaluation based on NLP application or research
  • Class presentation or demo of the final project

Prerequisites: Basic programming skills (Python recommended) and familiarity with concepts in machine learning and statistics would be helpful. No specific NLP background is required, as foundational NLP concepts will be covered during the course.