Natural Language Generation

Practice Exam Review

Final Exam Review

In this module, we'll continue our exploration of linguistic analysis of sentences rather than focusing on the structure of sentences like we did on the parsing module. To do so, we'll cover logical representations of sentence meaning, semantic role labeling and information extraction.

In this module, we'll delve more into the linguistics side of natural language processing. We'll take a look at different approaches to parsing and learn about the structure of sentences from a linguistics perspective.

In this module, we will delve into two related NLP topics: dialogue systems and question answering.

In this module, we will go over machine translation, one of the most important NLP applications, the challenges it involves, and how to evaluate machine translation models.

In this module, we will delve into some of the capabilities of cutting edge pre-trained language models. We will explore the vital concepts of prompt engineering and instruction following. We'll first discuss the pre-train, prompt and predict paradigm, a new approach which questions whether fine tuning is even necessary. After. we'll cover several advanced prompting techniques such as few shot learning, instruction following and chain of thought prompting. These advanced techniques will provide us with a more efficient and more creative ways to harness the full potential of pre-trained language models.

In this module, we will cover encoder-decoder models, BERT, fine-tuning and masked language models. Understanding them will give you a good understanding of state-of-the-art NLP models, and why pre-trained large language models have become so important.

In this module, we will cover transformers and pre-trained language models, and text generation. For the latter section, we will be joined by guest lecturer and Penn PhD graduate, Dr. Daphne Ippolito.

In this module, we're going to cover part of speech tagging. This is a fundamental task in natural language processing and has traditionally been used in a variety of applications. We'll cover some of the models that are used for part of speech tagging, going into some depth into hidden Markov models and dynamic programing, which is a technique widely useful to avoid exponential complexity and discrete optimization problems.