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arXiv · 1808.08316

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Tu Nguyen·Tuan Tran·Wolfgang Nejdl
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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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Tu Nguyen, Tuan Tran, Wolfgang Nejdl. 2018-08-24. A Trio Neural Model for Dynamic Entity Relatedness Ranking. https://arxiv.org/abs/1808.08316

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