  The task of Fine-grained Entity Type Classification (FETC) consists of
assigning types from a hierarchy to entity mentions in text. Existing methods
rely on distant supervision and are thus susceptible to noisy labels that can
be out-of-context or overly-specific for the training sentence. Previous
methods that attempt to address these issues do so with heuristics or with the
help of hand-crafted features. Instead, we propose an end-to-end solution with
a neural network model that uses a variant of cross- entropy loss function to
handle out-of-context labels, and hierarchical loss normalization to cope with
overly-specific ones. Also, previous work solve FETC a multi-label
classification followed by ad-hoc post-processing. In contrast, our solution is
more elegant: we use public word embeddings to train a single-label that
jointly learns representations for entity mentions and their context. We show
experimentally that our approach is robust against noise and consistently
outperforms the state-of-the-art on established benchmarks for the task.
