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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 21

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

Named Entity Recognition and Named Entity Linking on Esports Contents

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DOI: http://dx.doi.org/10.15439/2020F24

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 189192 ()

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Abstract. We built a named entity recognition/linking system on Esports News. We established an ontology for Esports-related entities, collected and annotated corpus from 80 articles on 4 different Esports titles, trained CRF and BERT-based entity recognizer, built a basic Dota2 knowledge base and a Entity linker that links mentions to articles in Liquipedia, and an end-to-end web app which serves as a demo of this entire proof-of-conecpt system. Our system achieved an over 61\% overall entity-level F1-score on the test set for the NER task, and also satisfying intuitive results on the linking task.


  1. N. Chinchor and P. Robinson, “Muc-7 named entity task definition,” in Proceedings of the 7th Conference on Message Understanding, vol. 29, 1997, pp. 1–21.
  2. D. Rao, P. McNamee, and M. Dredze, “Entity linking: Finding extracted entities in a knowledge base,” in Multi-source, multilingual information extraction and summarization. Springer, 2013, pp. 93–115.
  3. T. Yao, W. Ding, and G. Erbach, “Chiners: a chinese named entity recognition system for the sports domain,” in Proceedings of the second SIGHAN workshop on Chinese language processing, 2003, pp. 55–62.
  4. C.-K. Lee and M.-G. Jang, “Named entity recognition with structural svms and pegasos algorithm,” Korean Journal of Cognitive Science, vol. 21, no. 4, pp. 655–667, 2010.
  5. X. Seti, A. Wumaier, T. Yibulayin, D. Paerhati, L. Wang, and A. Saimaiti, “Named-entity recognition in sports field based on a character-level graph convolutional network,” Information, vol. 11, no. 1, p. 30, 2020.
  6. J. Lafferty, A. McCallum, and F. C. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” 2001.
  7. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint https://arxiv.org/abs/1810.04805, 2018.