CohEED^istributed

This project aims at enabling the entity recognition and alignment of huge text collections with dozens of millions of documents. To reach this goal, this distributed implementation of CohEEL is built on the Apache Flink framework and the applied knowledge base is Wikipedia. The source code is available on GitHub.

• Toni Gruetze
• Stefan Bunk
• Gjergji Kasneci
• Zhe Zuo
• Felix Naumann

News: The news article dataset contains 100 randomly picked Reuters articles from the CoNLL-YAGO dataset [1]. The articles were carefully manually annotated with entities from YAGO by our team members and can be found here.

Encyclopedic: The encyclopedic text corpus consists of Wikipedia articles selected in 2006 by Silviu Cucerzan [2]. The original annotations are available here. However, some of the original Wikipedia articles were missing and the YAGO alignments had to be determined. The updated dataset with the annotated YAGO entities can be found here.

Micro: The synthetic micro corpus consists of 50 short text snippets and was introduced in the AIDA project [4]. Every text snippet consists of few (usually one) hand-crafted sentences about different ambiguous mentions of named entities and has similar properties as content of microblogging platforms, such as Twitter. It was produced in the realm of the AIDA project and is available as the KORE dataset here.

[1] Z. Zuo, G. Kasneci, T. Gruetze, F. Naumann. BEL: Bagging for Entity Linking. In Proceedings of the International Conference on Computational Linguistics (COLING), pages 2075–2086, 2014.

[2] S. Cucerzan. Large-scale named entity disambiguation based on Wikipedia data. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 708–716, 2007.

[3] J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum. Robust disambiguation of named entities in text. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 782–792, 2011.

[4] J. Hoffart, S. Seufert, D. B. Nguyen, M. Theobald, and G. Weikum. KORE: keyphrase overlap relatedness for entity disambiguation. In Proceedings of the International Conference on Information and Knowledge Management (CIKM), pages 545–554, 2012.