Abstract
The purpose of a joint entity-relation extraction task is to extract entity-relation triples from unstructured text to assist text analysis, knowledge graph construction, etc. The existing sequence-to-sequence or sequence-to-non-sequence models treat the joint extraction task as a triple generation task, sharing the feature space of entity and relation extraction in the same structure. However, fusing the information of both subtasks may cause the problem of feature conflicts and thus decrease model performance. In order to enable each extraction subtask has its own independent feature space to reduce feature conflicts, this paper proposes a dual-decoder to decode entity extraction subtask and relation extraction subtask separately based on an encoder-to-decoder structure. A Dual-Joint-Input-PFN model is proposed by improving the partition filter network as an interaction to capture connection information between two subtasks. The model consists of two Joint-Input-PFNs layers, and each layer accepts two inputs simultaneously and filters the other input according to one of them. The experiments are based on standard datasets WebNLG and NYT, and the effectiveness of the proposed model is verified by comparing with the state-of-the-art baseline methods.
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Acknowledgements
The work is supported by grants from National Natural Science Foundation of China (No. 61871141), Natural Science Foundation of Guangdong Province (2021A1515011339), and Collaborative Innovation Team of Guangzhou University of Traditional Chinese Medicine (No. 2021XK08).
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Huang, Z., Liang, L., Zhu, X., Weng, H., Yan, J., Hao, T. (2022). An Improved Partition Filter Network for Entity-Relation Joint Extraction. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_10
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