@InProceedings{nguyen-tran-nejdl:2018:K18-1,
  author    = {Nguyen, Tu  and  Tran, Tuan  and  Nejdl, Wolfgang},
  title     = {A Trio Neural Model for Dynamic Entity Relatedness Ranking},
  booktitle = {Proceedings of the 22nd Conference on Computational Natural Language Learning},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {31--41},
  abstract  = {Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and 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 network-based approach that leverages public 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.},
  url       = {http://www.aclweb.org/anthology/K18-1004}
}