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.
@article{arxiv.1506.07650,
title = {Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling},
author = {Kun Xu and Yansong Feng and Songfang Huang and Dongyan Zhao},
journal= {arXiv preprint arXiv:1506.07650},
year = {2015}
}