Out of vocabulary modeling is a very common technique to handle non frequent entities in a training dataset. The usual approach in deep learning is to assign OOV embedding that is shared between all the non-frequent entities. Sharing one vector representation among very different entities is inefficient. In this lecture i will show how to create more classes of OOV and how to assign each OOV entity to a different cluster by learning to classify rare data points to cluster.