End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

摘要

We address fine-grained entity classifica tion and propose a novel attention-based recurrent neural network (RNN) encoder-decoder 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 fiat or local clas sifiers that do not directly model hierarchi cal structure.