UW Centre of Excellence

Natural Language Processing

• Introduction to NLP and its Applications
• Basics of Linguistics and Language Representation
• Text Preprocessing Techniques: Tokenization, Stop word Removal, Stemming and Lemmatization, Part-of-Speech Tagging
• Feature Engineering for NLP Tasks: Bag-of-Words Model, TF-IDF (Term Frequency-Inverse Document Frequency), Word Embeddings (e.g., Word2Vec, GloVe)
• Sentiment Analysis: Understanding Sentiment Analysis Tasks, Lexicon-based Approaches, Machine Learning-based Approaches
• Named Entity Recognition (NER)
• Text Classification: Supervised Learning Approaches (e.g., Naive Bayes, Support Vector Machines, Neural Networks), Evaluation Metrics (e.g., Accuracy, Precision, Recall, F1-score)
• Sequence Labeling: Hidden Markov Models (HMM), Conditional Random Fields (CRF)