Named Entity Recognition and Named Entity Linking on Esports Contents

• Autorzy: Liu Ziyu , Leng Yifan , Wang Meiqi , Lin Congzhu

Identyfikatory

DOI

10.15439/2020F24

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

natural language processing; ontology; artificial intelligence; sport; text analysis

PL

przetwarzanie języka naturalnego; ontologia; sztuczna inteligencja; sport; analiza tekstu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 189--192
• Opis fizyczny: Bibliogr. 7 poz., tab., il.

Twórcy

autor

Liu Ziyu

• Michtom School of Computer Science, Brandeis University Waltham, Massachusetts, USA

autor

Leng Yifan

• Michtom School of Computer Science, Brandeis University Waltham, Massachusetts, USA

autor

Wang Meiqi

• Michtom School of Computer Science, Brandeis University Waltham, Massachusetts, USA

autor

Lin Congzhu

• Michtom School of Computer Science, Brandeis University Waltham, Massachusetts, USA

Bibliografia

• 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.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 5th International Workshop on Language Technologies and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).