Abstract
When extracting entity relations from unstructured Chinese texts, it is necessary to address the problems of relation overlap and entity overlap that may occur within a single sentence. Current mainstream entity relation extraction strategies mostly focus on the influence of syntactic structure information, while ignoring the potential relationships between the syntactic structure of the text and overlapping entity relation triples. A model based on the Syntactic Dependency Tree and Multi-dimensional Corner Marking strategy (SDT-MCM) is proposed for overlapping entity relation extraction in this paper. Firstly, SDT-MCM constructs a character adjacency graph based on its syntactic dependency tree. And graph convolutional neural networks (GCNs) are utilized to extract syntactic features from the character adjacency graph. These are then combined with the semantic features extracted by BERT to form joint features. A novel multidimensional corner marking strategy is introduced to annotate overlap situations. Finally, entities and relations are jointly extracted using a score-based classifier. To validate the effectiveness of SDT-MCM, experiments on five public datasets including DuIE2.0, CMeIE, FinRE, SanWen, and CHIP2022 were conducted for entity relation extraction and overlap entity-relation comparison. SDT-MCM achieved F1 scores of 75.25%, 45.30%, and 36.42% on DuIE2.0, FinRE, and SanWen datasets respectively, outperforming all the compared models.
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Acknowledgement
This paper is the results of the research project funded by the China Fundamental Research Funds for the Central Universities (No:2662022XXYJ001, 2662022JC004, 2662023XXPY005). Partly supported by the open funds of the National Key Laboratory of Crop Genetic Improvement (ZK202205).
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Liu, X. et al. (2024). Overlapping Entity Relation Extraction Based on Syntactic Dependency Tree and Multi-dimensional Corner Marking Strategy. In: Huang, DS., Si, Z., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14877. Springer, Singapore. https://doi.org/10.1007/978-981-97-5669-8_18
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