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Entity Set Expansion (ESE) aims to identify new entities belonging to the same semantic class as the given set of seed entities.
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Y. Wang, H. Huang, and C. Feng, “Query expansion with local conceptual word embeddings in microblog retrieval,” IEEE Transactions on Knowledge and Data Engineering , vol. 33, no. 4, pp. 1737–1749, 2019
2019
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P. Yu, Z. Huang, R. Rahimi, and J. Allan, “Corpus-based set expansion with lexical features and distributed representations,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2019, pp. 1153–1156
2019
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Y. Zhang, J. Shen, J. Shang, and J. Han, “Empower entity set expansion via language model probing,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , 2020, pp. 8151–8160
2020
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R. Kohita, I. Yoshida, H. Kanayama, and T. Nasukawa, “Interactive construction of user-centric dictionary for text analytics,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , 2020, pp. 789–799
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C. Wu, F. Wu, Y. Huang, and X. Xie, “Neural news recommendation with negative feedback,” CCF Transactions on Pervasive Computing and Interaction , vol. 2, pp. 178–188, 2020
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J. Huang, Y. Xie, Y. Meng, J. Shen, Y. Zhang, and J. Han, “Guiding corpus-based set expansion by auxiliary sets generation and co-expansion,” in Proceedings of The Web Conference 2020 , ser. WWW ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 2188–2198. [Online]. Available: https://doi.org/10.1145/3366423.3380284
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Y. Li, Y. Li, Y. He, T. Yu, Y. Shen, and H.-T. Zheng, “Contrastive learning with hard negative entities for entity set expansion,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 1077–1086
2022
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2023
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N. Li, Z. Bouraoui, and S. Schockaert, “Ultra-fine entity typing with prior knowledge about labels: A simple clustering based strategy,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 11 744–11 756
2023
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2023
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J. Xiao, M. Elkaref, N. Herr, G. D. Mel, and J. Han, “Taxonomy-guided fine-grained entity set expansion,” in Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) . SIAM, 2023, pp. 631–639
2023
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Z. Ji, T. Yu, Y. Xu, N. Lee, E. Ishii, and P. Fung, “Towards mitigating llm hallucination via self reflection,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 1827–1843
2023
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X. Cheng, Z. Zhu, W. Xu, Y. Li, H. Li, and Y. Zou, “Accelerating multiple intent detection and slot filling via targeted knowledge distillation,” in The 2023 Conference on Empirical Methods in Natural Language Processing , 2023
2023
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Y. Li, T. Lu, H.-T. Zheng, Y. Li, S. Huang, T. Yu, J. Yuan, and R. Zhang, “Mesed: A multi-modal entity set expansion dataset with fine-grained semantic classes and hard negative entities,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 8, 2024, pp. 8697–8706
2024
Closest in time.
T. Komarlu, M. Jiang, X. Wang, and J. Han, “Ontotype: Ontology-guided and pre-trained language model assisted fine-grained entity typing,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 1407–1417
2024
Closest in time.