Joint entity and relation extraction based on specific-relation attention mechanism and global features

Zijia Ren

doi:10.1117/12.2685546

15 August 2023 Joint entity and relation extraction based on specific-relation attention mechanism and global features

Zijia Ren

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127192R (2023) https://doi.org/10.1117/12.2685546
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

In natural language processing, the research on extracting triple relations from unstructured texts to construct knowledge graphs has been widely concerned, however, few existing methods can solve the problem of overlapping triples and pay little attention to relations interaction with sentences. Besides, because entities have multiple forms, most methods rely on contextual features during entity recognition, and these models often misrepresent global relations. These all affect the accuracy of triplet extraction. To address the above issues, this paper proposes a joint extraction model with specific relation embeddings as well as incorporating global relations. It is called SRGF. Specifically, the model first converts the text into a vector representation that combines contextual features and global feature vectors through a pre-trained model and a graph convolutional network GCN, and then extracts topics. So entity extraction can be enhanced. An attention network guided by subject and relation information is employed to learn improved sentence representations for identifying the related objects. By doing so, it becomes possible to capture and model the interactions among entities, relations, and sentences, thus leading to better solutions for the problem of overlapping triples. Experimental results on public datasets NYT and WebNLG demonstrate that SRGF outperforms previous methods in triplet extraction.

Citation Download Citation

Zijia Ren "Joint entity and relation extraction based on specific-relation attention mechanism and global features", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127192R (15 August 2023); https://doi.org/10.1117/12.2685546

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KEYWORDS

Feature extraction

Feature fusion

Matrices

Semantics

Data modeling

Education and training

Process modeling

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