  Entity typing (ET) is the problem of assigning labels to given entity
mentions in a sentence. Existing works for ET require knowledge about the
domain and target label set for a given test instance. ET in the absence of
such knowledge is a novel problem that we address as ET in the wild. We
hypothesize that the solution to this problem is to build supervised models
that generalize better on the ET task as a whole, rather than a specific
dataset. In this direction, we propose a Collective Learning Framework (CLF),
which enables learning from diverse datasets in a unified way. The CLF first
creates a unified hierarchical label set (UHLS) and a label mapping by
aggregating label information from all available datasets. Then it builds a
single neural network classifier using UHLS, label mapping, and a partial loss
function. The single classifier predicts the finest possible label across all
available domains even though these labels may not be present in any
domain-specific dataset. We also propose a set of evaluation schemes and
metrics to evaluate the performance of models in this novel problem. Extensive
experimentation on seven diverse real-world datasets demonstrates the efficacy
of our CLF.
