Abstract
Relation extraction (RE) is an important part of knowledge graph construction. The span-based entity-relation joint extraction model is an emerging model for Relation extraction. In the span-based entity-relation joint extraction model, the method of generating span representation vectors is usually relatively simple, and the semantic representation ability is insufficient. This paper studies the impact of four different span vector representation methods on the performance of the entity-relation joint extraction model, and enriches the features of span representation vectors by combining multiple span semantic representation methods. Compared with the baseline model, the combined span representation method can effectively improve the performance of the model on the CoNLL04 data set. Named entity recognition has achieved an F1 score of 89.37%, and relation extraction has achieved an F1 score of 72.64%. Compared with the baseline model, it has increased by 0.43% and 1.17% respectively.
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Tang, Y., Yu, J., Li, S., ji, B., Tan, Y., Wu, Q. (2021). Span Representation Generation Method in Entity-Relation Joint Extraction. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_39
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