2017
EACL
EACL 2017
End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Hot Topic Early Bird
— hierarchical classification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio