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A Trio Neural Model for Dynamic Entity Relatedness Ranking
2018
Proceedings of the 22nd Conference on Computational Natural Language Learning
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
doi:10.18653/v1/k18-1004
dblp:conf/conll/NguyenTN18
fatcat:liasz2zhpnbnjmqh5dculu4lfa