Relation Classification via CNN, Segmented Max-pooling, and SDP-BLSTM

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)

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.

Keywords

Relation classification Deep learning CNN LSTM 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Data Science, School of Computer ScienceFudan UniversityShanghaiChina

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