%0 Conference Paper %1 nguyen2018neural %A Nguyen, Tu Ngoc %A Tran, Tuan %A Nejdl, Wolfgang %B Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL 2018) %D 2018 %K alexandria l3s myown sysrelevantforl3s %R 10.18653/v1/K18-1004 %T A Trio Neural Model for Dynamic Entity Relatedness Ranking %U https://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.

@inproceedings{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 = {2018-07-27T14:49:25.000+0200}, author = {Nguyen, Tu Ngoc and Tran, Tuan and Nejdl, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/2688aa8a4e7214a400b72868ce58efcc6/tumeteor}, booktitle = {Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL 2018)}, doi = {10.18653/v1/K18-1004}, interhash = {1ac6d5b8a7cc0b5ab7e8e575095917cd}, intrahash = {688aa8a4e7214a400b72868ce58efcc6}, keywords = {alexandria l3s myown sysrelevantforl3s}, organization = {ACL}, timestamp = {2018-10-26T00:00:23.000+0200}, title = {A Trio Neural Model for Dynamic Entity Relatedness Ranking}, url = {https://arxiv.org/abs/1808.08316}, year = 2018 }