Businesses run on communication - customers reach out when they want something, colleagues communicate to get work done. Every message counts. Our mission at Re:infer is to unlock the value in these messages and to help every team in a business deliver better products and services efficiently and at scale.
With that goal, we continuously research and develop our core machine learning and natural language understanding technology. The machine learning models at Re:infer use pre-training, unsupervised learning, semi-supervised learning and active learning to deliver state of the art accuracy with minimal time and investment from our users.
In this research post, we explore a new unsupervised approach to automatically recognising the topics and intents, and their taxonomy structure, from a communications dataset. It's about improving the quality of the insights we deliver and the speed with which these are obtained.