ES-LDA: Entity Summarization using Knowledge-Based Topic Modeling
Proceedings of the International Joint Conference on Natural Language Processing
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.
Pouriyeh, Seyedamin, Mehdi Allahyari, Krys Kochut, Gong Cheng, Hamid Reza Arabina.
"ES-LDA: Entity Summarization using Knowledge-Based Topic Modeling."
Proceedings of the International Joint Conference on Natural Language Processing, Greg Kondrak and Taro Watanabe (Ed.): 316-325 Taipei, Taiwan: Asian Federation of Natural Language Processing.