Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling.
K. Xu, Y. Feng, S. Huang, and D. Zhao. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing EMNLP, page 536–540. (2015)cite arxiv:1506.07650.
Syntactic features play an essential role in identifying relationship in a
sentence. Previous neural network models often suffer from irrelevant
information introduced when subjects and objects are in a long distance. In
this paper, we propose to learn more robust relation representations from the
shortest dependency path through a convolution neural network. We further
propose a straightforward negative sampling strategy to improve the assignment
of subjects and objects. Experimental results show that our method outperforms
the state-of-the-art methods on the SemEval-2010 Task 8 dataset.
Semantic Relation Classification via Convolutional Neural Networks with
Simple Negative Sampling