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Hosted by Keelin M · 🇺🇸 US · EN · 16 episodes
Established thought leaders with verified media credentials.
The basics of natural language generation (NLG), based on the curriculum of CIS 5300 – and created with a little help from NotebookLM ! All episode cover images created with Flux.1-schnell
Keelin M hosts Natural Language Generation, a technology show with 16 episodes published.
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 se
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-tra
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
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
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 for their contexts. T
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 for their contexts. T
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 discuss a variety of goa
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 represent words in a way tha
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 of the simpler langua
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 we've preprocessed our
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 attitudes that an autho
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Natural Language Generation is hosted by Keelin M. The show is categorised under technology and has published 16 episodes.
Natural Language Generation has published 16 episodes.
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