Krzysztof Kochut

Also published as: K. Kochut


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ES-LDA: Entity Summarization using Knowledge-based Topic Modeling
Seyedamin Pouriyeh | Mehdi Allahyari | Krzysztof Kochut | Gong Cheng | Hamid Reza Arabnia
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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.


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Natural Language Information Retrieval System Dialog
L. Bole | K. Kochut | A. Lesniewski | T. Strzalkowski
First Conference of the European Chapter of the Association for Computational Linguistics