Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling.
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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.
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