ES-LDA: Entity Summarization using Knowledge-based Topic Modeling
Seyedamin Pouriyeh, Mehdi Allahyari, Krzysztof Kochut, Gong Cheng, Hamid Reza Arabnia
Abstract
With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real-world entities, has gained increasing attention in recent years. In this paper, we propose a probabilistic topic model, ES-LDA, that combines prior knowledge with statistical learning techniques within a single framework to create more reliable and representative summaries for entities. We demonstrate the effectiveness of our approach by conducting extensive experiments and show that our model outperforms the state-of-the-art techniques and enhances the quality of the entity summaries.- Anthology ID:
- I17-1032
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 316–325
- Language:
- URL:
- https://aclanthology.org/I17-1032
- DOI:
- Cite (ACL):
- Seyedamin Pouriyeh, Mehdi Allahyari, Krzysztof Kochut, Gong Cheng, and Hamid Reza Arabnia. 2017. ES-LDA: Entity Summarization using Knowledge-based Topic Modeling. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 316–325, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- ES-LDA: Entity Summarization using Knowledge-based Topic Modeling (Pouriyeh et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/I17-1032.pdf