Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification
arXiv:1707.01265
Abstract
Most of neural approaches to relation classification have focused on finding short patterns that represent the semantic relation using Convolutional Neural Networks (CNNs) and those approaches have generally achieved better performances than using Recurrent Neural Networks (RNNs). In a similar intuition to the CNN models, we propose a novel RNN-based model that strongly focuses on only important parts of a sentence using multiple range-restricted bidirectional layers and attention for relation classification. Experimental results on the SemEval-2010 relation classification task show that our model is comparable to the state-of-the-art CNN-based and RNN-based models that use additional linguistic information.
6 pages, 1 figure
References in corpus (7)
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Improving neural networks by preventing co-adaptation of feature detectors
- ADADELTA: An Adaptive Learning Rate Method
- Relation Classification via Recurrent Neural Network
- End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
- Classifying Relations by Ranking with Convolutional Neural Networks
- Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path