ES-LDA: Entity Summarization using Knowledge-Based Topic Modeling
Contribution to Book
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.