Neural Architectures for Fine-grained Entity Type Classification

Understand

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.