Iris.ai uses NLP to review collections of research papers and patents, in order to find the right documents, extract key data and identify pieces of knowledge. Iris.ai uses a combination of keyword extraction, word embeddings, neural topic modeling, word importance based similarity of document metrics and hierarchical topic modeling. The approach is mainly unsupervised but we utilize an evaluated annotation set from our community of AI Trainers for benchmarking and improving our tools.
