TY - GEN

T1 - A Trio Neural Model for Dynamic Entity Relatedness Ranking

AU - Nguyen, Tu Ngoc

AU - Tran, Tuan

AU - Nejdl, Wolfgang

N1 - Conference code: 22

PY - 2018/10

Y1 - 2018/10

N2 - 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.

AB - 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.

KW - cs.IR

KW - cs.CL

KW - cs.LG

KW - stat.ML

U2 - 10.18653/v1/K18-1004

DO - 10.18653/v1/K18-1004

M3 - Conference contribution

SP - 31

EP - 41

BT - Proceedings of the 22nd Conference on Computational Natural Language Learning

CY - Brüssel

T2 - Conference on<br/>Computational Natural Language Learning

Y2 - 31 October 2018 through 1 November 2018

ER -