Neural Architectures for Fine-grained Entity Type Classification Neural Architectures for Fine-grained Entity Type Classification
Paper summary They propose a neural architecture for assigning fine-grained labels to detected entity types. The model combines bidirectional LSTMs, attention over the context sequence, hand-engineered features, and the label hierarchy. They evaluate on Figer and OntoNotes datasets, showing improvements from each of the extensions. https://i.imgur.com/HJL3CYy.png

Summary by Marek Rei 10 months ago
Loading...
Your comment:


ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!
About

Sponsored by: and