Simple Customization of Recursive Neural Networks for Semantic Relation Classification

Kazuma Hashimoto, Makoto Miwa, Yoshimasa Tsuruoka, Takashi Chikayama

Abstract: In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average parameters in training. Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model. We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity. The proposed model marks scores competitive with state-of-the-art RNN-based models.