Abstract
Relational triples extraction aims to detect entity pairs (subjects, objects) along with their relations. Previous work failed to deal with complex relationship triples, such as overlapping triples and nested entities, and lacked semantic representation in the process of extracting entity pairs and relationships. To mitigate these issues, we propose a joint extraction model called ReMERT, which first decomposes the joint extraction task into three interrelated subtasks, namely RSE (Relation-specific Subject Extraction), RM (Relational Memory) module construction and OE (Object Extraction). The first subtask is to distinguish all subjects that may be involved with target relations, the second is to retrieve target relational representation from RM module, and the last is to identify corresponding objects for each specific (s, r) pair. Additionally, RSE and OE subtasks are further deconstructed into sequence labeling problems based on the proposed hierarchical binary tagging scheme. Owing to the reasonable decomposition strategy, the proposed model can fully capture the semantic interdependency between different subtasks, as well as reduce noise from irrelevant entity pairs. Experimental results show that the proposed method outperforms previous work by 0.8% (F1 score), achieving a new state-of-the-art on Chinese DuIE datasets. We also adopt sufficient experiments and obtain promising results both in public English NYT and Chinese DuIE datasets.
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Acknowledgements
We thanks anonymous reviewers for their precious comments. This research is supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant SJCX21_0989) and Smart Mining Open Funding Project of Shandong Energy Zibo Mining Group & China University of Mining and Technology (Grant 2019LH10).
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Zhao, C., Dai, X., Feng, L., Liu, P. (2021). ReMERT: Relational Memory-Based Extraction for Relational Triples. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_24
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DOI: https://doi.org/10.1007/978-3-030-88480-2_24
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