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About This Podcast
The basics of natural language generation (NLG), based on the curriculum of CIS 5300 – and created with a little help from NotebookLM!
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Recent Episodes
S1E16 Practice Exam Review
Practice Exam Review
S1E15 Final Exam Review
Final Exam Review
S1E14 Logical Representations of Sentence Meaning, Semantic Role Labeling & Information Extraction
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…
S1E13 Parsing and Dependency Parsing
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…
S1E12 Dialogue Systems, Chatbots & Question Answering
In this module, we will delve into two related NLP topics: dialogue systems and question answering.
S1E11 Machine Translation
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.
S1E10 Prompt Engineering, Instruction Following, and Using GPT
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…
S1E9 Encoder-Decoders, BERT and Fine-tuning
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…
S1E8 Transformers and Neural Text Generation
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.
S1E7 Parts of Speech & Grammars
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…
S1E6 Neural Language Models
In this module, we'll take a look at neural network based language models, which, unlike the previous N-gram based language models that we looked at earlier, use word embedding based representations…
S1E5 Vector Space Models
This week, we will continue our exploration of vector space semantics and embeddings. We'll begin the module by wrapping up word embeddings and discussing bias in vector space models. Then, we'll…
S1E4 Vector Space Semantics
In this module, we'll begin to explore vector space semantics in natural language processing. (This will continue into next week.) Vector space semantics are powerful because they allow us to…
S1E3 Review of Probabilities & N-gram Language Models
In this module, we are going to cover essential topics that will allow us to move into important tasks in NLP: a review of probability and defining a probabilistic model. We will then delve into one…
S1E2 Text Processing & Logistic Regression
In this module, we'll begin with delving into text preprocessing. We'll go through tasks that transform an unstructured text into a structured format that we can analyze via machine learning. Once…
S1E1 Text Classification, Sentiment Analysis, and Regular Expressions
In this module, we’ll get started by looking at a classic natural language processing problem: text classification. Using the example of sentiment analysis, where we can determine the emotions and…
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Natural Language Generation has published 16 episodes since January 2025, covering topics in Technology.
Natural Language Generation is currently dormant with new episodes weekly. Average episode length is 20m.
