End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Sanjeev Kumar Karn, Ulli Waltinger, Hinrich Schütze · 2017
We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder that generates paths in the type hierarchy and can be trained end-to-end.We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.