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 goals that any representation of word meaning should aim to achieve. These six goals will help us understand different aspects of word meaning and the relationships of words with other words. Then, we'll pivot to a coding demo that will provide you with a hands on experience working with vector space models and see how word embeddings can be used to retrieve words with similar meaning and to solve word analogy tasks.

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. This allows them to make much better probabilistic prediction about the next word in a sequence, and they have become the foundation for large pre-trained language models like Chat GPT that have led to exciting innovations in the field of NLP.

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 that allows us to measure similarity between words and capture several other kinds of meaning. We'll start this module by exploring important concepts that underpin this topic, like the distributional hypothesis and term-by-document matrices, and then switch to cover a recent approach to vector space models called word embeddings

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 language models, the N-gram language model.

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 text data, we can then move on to building machine learning models for text classification tasks. To do this, we'll introduce logistic regression, which is a popular algorithm for text classification, and explain the concept of gradient descent.

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 author expresses towards the subject of their writing, we will delve into classifying text. Also this week, we will introduce regular expressions, a powerful tool for natural language processing.