Semantic Map Embeddings – Part II

I originally published this on the Rasa blog. In Part I we introduced semantic map embeddings and their properties. Now it’s time to see how we create those embeddings in an unsupervised way and how they might improve your NLU pipeline. Training Semantic Maps At the heart of our training procedure …

Semantic Map Embeddings – Part I

I originally published this on the Rasa blog. How do you convey the “meaning” of a word to a computer? Nowadays, the default answer to this question is “use a word embedding”. A typical word embedding, such as GloVe or Word2Vec, represents a given word as a real vector of …

Self-Organizing Maps

Recently, I discovered this neat little algorithm called “self-organizing maps” that can be used to create a low-dimensional “map” (as in cartography) of high-dimensional data. The algorithm is very simple. Say you have a set of high-dimensional vectors and you want to represent them in an image, such that each …

Representing concepts efficiently

This week’s post is about “Semantic Folding Theory and its Application in Semantic Fingerprinting” by Webber . The basic ideas were also discussed in this Braininspired podcast, and also presented and recorded at the HVB Forum in Munich. You don’t need any particular prior knowledge to understand this post, but …