  In this work, we investigate several neural network architectures for
fine-grained entity type classification. Particularly, we consider extensions
to a recently proposed attentive neural architecture and make three key
contributions. Previous work on attentive neural architectures do not consider
hand-crafted features, we combine learnt and hand-crafted features and observe
that they complement each other. Additionally, through quantitative analysis we
establish that the attention mechanism is capable of learning to attend over
syntactic heads and the phrase containing the mention, where both are known
strong hand-crafted features for our task. We enable parameter sharing through
a hierarchical label encoding method, that in low-dimensional projections show
clear clusters for each type hierarchy. Lastly, despite using the same
evaluation dataset, the literature frequently compare models trained using
different data. We establish that the choice of training data has a drastic
impact on performance, with decreases by as much as 9.85% loose micro F1 score
for a previously proposed method. Despite this, our best model achieves
state-of-the-art results with 75.36% loose micro F1 score on the well-
established FIGER (GOLD) dataset.
