A Trio Neural Model For Dynamic Entity Relatedness Ranking
Tu Nguyen, Tuan Tran, Wolfgang Nejdl . Proceedings of the 22nd Conference on Computational Natural Language Learning 2018 – 4 citations
[Paper]
Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time. In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.