A Trio Neural Model for Dynamic Entity Relatedness Ranking

• URL: http://arxiv.org/abs/1808.08316v4
• Date: Mon, 12 Jun 2023 20:49:49 GMT
• Title: A Trio Neural Model for Dynamic Entity Relatedness Ranking
• Authors: Tu Nguyen, Tuan Tran and Wolfgang Nejdl
• Abstract summary: We propose a neural networkbased approach for dynamic entity relatedness. Our model is capable of learning rich and different entity representations in a joint framework.
• Score: 1.4810568221629932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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