Vector embeddings are a way of representing words or concepts as sequences of numbers in a high-dimensional space. Unlike traditional methods of encoding words, such as simple dictionary definitions or one-hot encoding, vector embeddings capture subtle relationships and associations between words based on how they're used in context.
To understand the significance of vector embeddings, let's consider the famous quote from Shakespeare's Romeo and Juliet: "A rose by any other name would smell as sweet." This line beautifully illustrates that the essence of something isn't defined by its label or spelling, but by its inherent qualities and how we perceive it. Similarly, in the world of natural language processing and machine learning, vector embeddings aim to capture the essence of words beyond their surface-level representation.
When we look at a word like "joy", its spelling doesn't tell us much about its meaning or the emotions it evokes. The letters J-O-Y are merely a convention we use to represent the concept. Vector embeddings, on the other hand, attempt to capture the semantic meaning of "joy" by placing it in relation to other words and concepts in a multidimensional space. For computers, vector embeddings are more than just an alternative spelling system. They provide a rich, nuanced representation.
Word-vector arithmetic harkens memories of Tomas Mikolov in 2012 for the classic NLP enthusiasts. By analyzing the vector embedding of "joy", we can gain insights into how this concept relates to other emotions, experiences, and ideas in our language and culture. This analysis goes beyond simple synonym-finding; it can reveal subtle connections and associations that might not be immediately apparent from dictionary definitions or everyday usage. Today, I start the journey of programmatically identifying the emotional content of vectors for more effective dimensionality reduction to enable things like efficient real time evaluation of conversations by sharing some popular work that is similar. The core differentiator is an emphasis on isolating the emotional content from a specific embedding model’s output vectors compared to mapping the general relationships between words. The work includes a series of important hypotheses to be examined including the obvious question, “is there emotional content in a vector embedding?”
Please take these two links and an image as an exciting preview of what is to come in the filtering for joy series.
The first few joy datasets which are all openai text-embedding-3-large embeddings
ChatGPTs candid thoughts on it’s own embeddings: https://chatgpt.com/share/4c334d7c-2270-48c8-b224-e9adfd2680c3
Some interesting things to read for context since vector embeddings and such have been around for a while
An image of a Kmeans cluster plot from early analysis of joy and it’s synonyms