Fundamentals of Natural Language Processing

The field of natural language processing (NLP) aims at getting computers to perform useful and interesting tasks with human language. This course introduces students to the 3 pillars underlying modern NLP: probabilistic language models, simple neural networks with a focus on gradient based learning, and vector-based meaning representations in the form of word embeddings. At the end of the course, students will be able to implement and analyze probabilistic language models based on N-grams, text

• Analyze corpora to develop effective lexicons using subword tokenization.
• Develop language models that can assign probabilities to texts.
• Design, implement, and evaluate the effectiveness of text classifiers using gradient-based learning techniques.
• Design, implement and evaluate unsupervised methods for learning word embeddings.