%0 Generic %1 nguyen2018neural %A Nguyen, Tu Ngoc %A Tran, Tuan %A Nejdl, Wolfgang %D 2018 %K deep_learning embeddings relatedness %T A Trio Neural Model for Dynamic Entity Relatedness Ranking %U http://arxiv.org/abs/1808.08316 %X 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.

@misc{nguyen2018neural, abstract = {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.}, added-at = {2018-12-12T10:17:58.000+0100}, author = {Nguyen, Tu Ngoc and Tran, Tuan and Nejdl, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/2d3a8f7d0a3c0dea649ec2eb5be204103/dallmann}, description = {A Trio Neural Model for Dynamic Entity Relatedness Ranking}, interhash = {1ac6d5b8a7cc0b5ab7e8e575095917cd}, intrahash = {d3a8f7d0a3c0dea649ec2eb5be204103}, keywords = {deep_learning embeddings relatedness}, note = {cite arxiv:1808.08316Comment: In Proceedings of CoNLL 2018}, timestamp = {2018-12-12T10:17:58.000+0100}, title = {A Trio Neural Model for Dynamic Entity Relatedness Ranking}, url = {http://arxiv.org/abs/1808.08316}, year = 2018 }