A Trio Neural Model for Dynamic Entity Relatedness Ranking
Tu Ngoc Nguyen, Tuan Tran, Wolfgang Nejdl · 2018
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.