  Inferring semantic types for entity mentions within text documents is an
important asset for many downstream NLP tasks, such as Semantic Role Labelling,
Entity Disambiguation, Knowledge Base Question Answering, etc. Prior works have
mostly focused on supervised solutions that generally operate on relatively
small-to-medium-sized type systems. In this work, we describe two systems aimed
at predicting type information for the following two tasks, namely, a
TypeSuggest module, an unsupervised system designed to predict types for a set
of user-entered query terms, and an Answer Type prediction module, that
provides a solution for the task of determining the correct type of the answer
expected to a given query. Our systems generalize to arbitrary type systems of
any sizes, thereby making it a highly appealing solution to extract type
information at any granularity.
