Abstract
Span-based joint extraction simultaneously conducts named entity recognition(NER)and re-lation extraction(RE)in a text span form.However,since previous span-based models rely on span-level classifications,they cannot benefit from token-level label information,which has been proven advantageous for the task.In this paper,we propose a sequence tagging augmented span-based network(STSN),a span-based joint model that can make use of token-level label information.In STSN,we construct a core neural architecture by deep stacking multiple attention layers,each of which consists of three basic attention units.On the one hand,the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction;on the other hand,it establishes a bi-directional information interaction between NER and RE.Experimental results on three benchmark datasets show that STSN consistently outperforms the strongest baselines in terms of F1,creating new state-of-the-art results.
基金项目
Hunan Provincial Natural Science Foundation(2022JJ30668)
Hunan Provincial Natural Science Foundation(2022JJ30046)
NETL
NSTL
万方数据