Abstract
Domain-specific relation extraction plays an important role in constructing domain knowledge graph and further analysis. In the field of military intelligence relation extraction, there are challenges such as relation overlapping and exposure bias. Therefore, on the basis of a combination of domain N-gram adapter and axial attention, this paper presents a single-step joint relation extraction model for the field of military text analysis. Considering domain-specific language structures and patterns, the domain-specific N-gram adapter is incorporated into the pre-trained language model to improve the encoding of the proposed model. Furthermore, the axial attention mechanism is applied to capture the dependencies between token pairs and their contexts, so as to enhance the encoding representation ability of the proposed model. After that, entities and relations are jointly extracted by a relation-specific decoding method. The effectiveness of the proposed model is demonstrated through experiments on a military relation extraction dataset with F1-Score 0.6690 and CMeIE with F1-Score 0.6051, which is better than existing joint relation extraction models.
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Yang, Z., Li, Z., Xu, Z., Gan, Z., Cao, W. (2023). A Joint Relation Extraction Model Based on Domain N-Gram Adapter and Axial Attention for Military Domain. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_20
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