Abstract
Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and 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 network-based approach that leverages public 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.- Anthology ID:
- K18-1004
- Volume:
- Proceedings of the 22nd Conference on Computational Natural Language Learning
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–41
- Language:
- URL:
- https://aclanthology.org/K18-1004
- DOI:
- 10.18653/v1/K18-1004
- Cite (ACL):
- Tu Nguyen, Tuan Tran, and Wolfgang Nejdl. 2018. A Trio Neural Model for Dynamic Entity Relatedness Ranking. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 31–41, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- A Trio Neural Model for Dynamic Entity Relatedness Ranking (Nguyen et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/K18-1004.pdf