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Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity Linking

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Natural Language Processing and Chinese Computing (NLPCC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14304))

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Abstract

Entity Linking (EL) is the task of grounding a textual mention in context to a corresponding entity in a knowledge base. However, current EL systems demonstrate a popularity bias, significantly underperforming on tail and emerging entities. To this end, we organize NLPCC 2023 Shared Task 6, i.e., Chinese Few-shot and Zero-shot Entity Linking, which aims at testing the generalization ability of Chinese EL systems to less popular and newly emerging entities. The dataset for this task is a human-calibrated and multi-domain Chinese EL benchmark with Wikidata as KB, consisting of few-shot and zero-shot test sets. There are 22 registered teams and 13 submissions in total, and the highest accuracy is 0.6915. The submitted approaches focus on different aspects of this problem and use diverse techniques to boost the performance. All relevant information can be found at https://github.com/HITsz-TMG/Hansel/tree/main/NLPCC.

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Notes

  1. 1.

    To facilitate searching, we provide annotators with pre-filled search query templates in an annotation tool, such as Google queries with entity names and target domains.

References

  1. Chen, A., Gudipati, P., Longpre, S., Ling, X., Singh, S.: Evaluating entity disambiguation and the role of popularity in retrieval-based NLP. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4472–4485. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.acl-long.345, https://aclanthology.org/2021.acl-long.345

  2. Curry, A.C., et al.: Alana v2: entertaining and informative open-domain social dialogue using ontologies and entity linking. Alexa Prize Proceedings (2018)

    Google Scholar 

  3. De Cao, N., Aziz, W., Titov, I.: Question answering by reasoning across documents with graph convolutional networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 2306–2317. Association for Computational Linguistics, Minneapolis, Minnesota (2019)

    Google Scholar 

  4. De Cao, N., et al.: Multilingual autoregressive entity linking. Trans. Assoc. Comput. Linguist. 10, 274–290 (2022)

    Article  Google Scholar 

  5. Févry, T., Baldini Soares, L., FitzGerald, N., Choi, E., Kwiatkowski, T.: Entities as experts: sparse memory access with entity supervision. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4937–4951. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.emnlp-main.400, https://www.aclweb.org/anthology/2020.emnlp-main.400

  6. Guu, K., Lee, K., Tung, Z., Pasupat, P., Chang, M.W.: REALM: retrieval-augmented language model pre-training. In: Proceedings of the 37th International Conference on Machine Learning. PMLR, Vienna, Austria (2020). https://proceedings.icml.cc/static/paper_files/icml/2020/3102-Paper.pdf

  7. Hu, B., Chen, Q., Zhu, F.: LCSTS: a large scale Chinese short text summarization dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1967–1972. Association for Computational Linguistics, Lisbon, Portugal (2015). https://doi.org/10.18653/v1/D15-1229, https://www.aclweb.org/anthology/D15-1229

  8. Logeswaran, L., Chang, M.W., Lee, K., Toutanova, K., Devlin, J., Lee, H.: Zero-shot entity linking by reading entity descriptions. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3449–3460. Association for Computational Linguistics, Florence, Italy (2019). https://doi.org/10.18653/v1/P19-1335, https://www.aclweb.org/anthology/P19-1335

  9. Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982–3992. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1410, https://aclanthology.org/D19-1410

  10. Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2), 443–460 (2014)

    Article  Google Scholar 

  11. Tsai, C.T., Roth, D.: Cross-lingual wikification using multilingual embeddings. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 589–598. Association for Computational Linguistics, San Diego, California (2016). https://doi.org/10.18653/v1/N16-1072, https://www.aclweb.org/anthology/N16-1072

  12. Wang, C., Zhang, M., Ma, S., Ru, L.: Automatic online news issue construction in web environment. In: Proceedings of the 17th International Conference on World Wide Web, pp. 457–466 (2008)

    Google Scholar 

  13. Zhang, C., Ré, C., Sadeghian, A., Shan, Z., Shin, J., Wang, F., Wu, S.: Feature engineering for knowledge base construction. IEEE Data Eng. Bull. (2014). http://arxiv.org/1407.6439arxiv.org/abs/1407.6439

  14. Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., Liu, Q.: ERNIE: enhanced language representation with informative entities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1441–1451. Association for Computational Linguistics, Florence, Italy (2019)

    Google Scholar 

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Acknowledgement

This work is jointly supported by grants: This work is jointly supported by grants: Natural Science Foundation of China (No. 62006061 and 82171475), Strategic Emerging Industry Development Special Funds of Shenzhen (No.JCYJ20200109113403826).

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Correspondence to Baotian Hu .

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Xu, Z., Shan, Z., Hu, B., Zhang, M. (2023). Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity Linking. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_23

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  • DOI: https://doi.org/10.1007/978-3-031-44699-3_23

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