AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction

Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.

Automated summary

This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. By defining a group of neuroimaging event-containing attributes and introducing a joint extraction model based on deep adversarial learning, the event extraction in a few-shot learning scenario is achieved. Experimental results demonstrate that the proposed method provides a practical approach for quickly collecting research information for neuroimaging provenance construction oriented to open research sharing.