An examples, consider attributes such as 'disputed territories' from the country domain, or 'supplier' from the organization domain; arguably the values of these attributes is so specific that numeric information cannot help.
in this paper, we propose a simple feedforward neural architecture to jointly predict numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question.