Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao · 2015
Syntactic features play an essential role in identifying relationship in a sentence.Previous neural network models directly work on raw word sequences or constituent parse trees, thus 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 shortest dependency paths through a convolution neural network.We further take the relation directionality into account and propose a straightforward negative sampling strategy to improve the assignment of subjects and objects.Experimental results show that our method outperforms the state-of-theart approaches on the SemEval-2010 Task 8 dataset.