Abstract
It is a crucial premise for named entity recognition task to achieve high-accuracy entity extraction. CCKS-2021 held a Knowledge Graph Fine-grained Entity Typing competition, and 262 teams participated. What is challenging in the task is the extremely large amounts of unlabeled data and the multi-label entity typing. In our approach, a semi-supervised learning strategy is conducted to cope with the unlabeled data, and a multi-label loss is employed to recognize the multi-label entity. An F1-score of 0.85498 on the final testing data is achieved, which verifies the performance of our approach, and ranks the second place in the task.
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Pu, K., Liu, H., Yang, Y., Lv, W., Li, J. (2022). Multi-label Fine-Grained Entity Typing for Baidu Wikipedia Based on Pre-trained Model. In: Qin, B., Wang, H., Liu, M., Zhang, J. (eds) CCKS 2021 - Evaluation Track. CCKS 2021. Communications in Computer and Information Science, vol 1553. Springer, Singapore. https://doi.org/10.1007/978-981-19-0713-5_13
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DOI: https://doi.org/10.1007/978-981-19-0713-5_13
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