He Zhao, Zhunchen Luo, Chong Feng, Anqing Zheng, Xiaopeng Liu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Network representation learning (NRL) is a crucial method to learn low-dimensional vertex representations to capture network information. However, conventional NRL models only regard each edge as a binary or continuous value while neglecting the rich semantic information on edges. To enhance network representation for Social Relation Extraction (SRE) task, we present a novel deep neural network based model, EsiNet, by learning the structure and semantic information of edges simultaneously. Compared with previous work, EsiNet focuses on further learning the interactions between vertices and capturing the correlations between labels. By jointly optimizing the objective function of these two components, EsiNet can preserve both the semantic and structural information of edges. Extensive experiments on several public datasets demonstrate that EsiNet outperforms other baselines significantly, by around 3% to 5% on hits@10 absolutely.