Abstract
Relation classification is the task of classifying the semantic relation between two marked entities in a sentence. This paper proposes a novel neural model for this task. It first does convolution on input sentence to get local features of words in local context windows, and then designs a novel segmented max-pooling to reduce the temporal dimension from the length of sentence to the length of shortest dependency path (SDP) between two marked entities, and finally, a SDP-BLSTM network is applied to produce the final fixed-size vector representation of the relation instance, which is fed to a two-layer feed-forward network for classification. Experiments on the SemEval-2010 Task 8 dataset show that our model achieves competitive performance when compared with several start-of-the-art models.
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Acknowledgments
This work is supported by National High-Tech R&D Program of China (863 Program) (No. 2015AA015404), and the 2016 Civil Aviation Safety Capacity Development Funding Project. We are grateful to the anonymous reviewers for their valuable comments.
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Wang, P., Xie, Z., Hu, J. (2017). Relation Classification via CNN, Segmented Max-pooling, and SDP-BLSTM. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_16
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