Sanjeev Karn


2017

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End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Sanjeev Karn | Ulli Waltinger | Hinrich Schütze
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We address fine-grained entity classification 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 flat or local classifiers that do not directly model hierarchical structure.