@inproceedings{nguyen-etal-2018-trio,
    title = "A Trio Neural Model for Dynamic Entity Relatedness Ranking",
    author = "Nguyen, Tu  and
      Tran, Tuan  and
      Nejdl, Wolfgang",
    editor = "Korhonen, Anna  and
      Titov, Ivan",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/K18-1004/",
    doi = "10.18653/v1/K18-1004",
    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."
}