  Fine-grained entity type classification (FETC) is the task of classifying an
entity mention to a broad set of types. Distant supervision paradigm is
extensively used to generate training data for this task. However, generated
training data assigns same set of labels to every mention of an entity without
considering its local context. Existing FETC systems have two major drawbacks:
assuming training data to be noise free and use of hand crafted features. Our
work overcomes both drawbacks. We propose a neural network model that jointly
learns entity mentions and their context representation to eliminate use of
hand crafted features. Our model treats training data as noisy and uses
non-parametric variant of hinge loss function. Experiments show that the
proposed model outperforms previous state-of-the-art methods on two publicly
available datasets, namely FIGER (GOLD) and BBN with an average relative
improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one
dataset can be transferred to other datasets while using same model or other
FETC systems. These approaches of transferring knowledge further improve the
performance of respective models.
