  Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity
mentions into a wide range of entity types (usually hundreds) depending upon
the context. While distant supervision is the most common way to acquire
supervised training data, it brings in label noise, as it assigns type labels
to the entity mentions irrespective of mentions' context. In attempts to deal
with the label noise, leading research on the FG-NET assumes that the
fine-grained entity typing data possesses a euclidean nature, which restraints
the ability of the existing models in combating the label noise. Given the fact
that the fine-grained type hierarchy exhibits a hierarchal structure, it makes
hyperbolic space a natural choice to model the FG-NET data. In this research,
we propose FGNET-HR, a novel framework that benefits from the hyperbolic
geometry in combination with the graph structures to perform entity typing in a
performance-enhanced fashion. FGNET-HR initially uses LSTM networks to encode
the mention in relation with its context, later it forms a graph to
distill/refine the mention's encodings in the hyperbolic space. Finally, the
refined mention encoding is used for entity typing. Experimentation using
different benchmark datasets shows that FGNET-HR improves the performance on
FG-NET by up to 3.5% in terms of strict accuracy.
