A Trio Neural Model for Dynamic Entity Relatedness Ranking

Tu Nguyen, Tuan Tran, Wolfgang Nejdl
2018 Proceedings of the 22nd Conference on Computational Natural Language Learning  
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
more » ... 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.
doi:10.18653/v1/k18-1004 dblp:conf/conll/NguyenTN18 fatcat:liasz2zhpnbnjmqh5dculu4lfa