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Sequence Labeling for Citation Field Extraction from Cyrillic Script References

Sequence Labeling for Citation Field Extraction from Cyrillic Script References

Published: 2022

Buchtitel: Proceedings of the AAAI Workshop on Scientific Document Understanding (SDU∂AAAI'22)
Verlag: ACM

Referierte Veröffentlichung


Extracting structured data from bibliographic references is a crucial task for the creation of scholarly databases. While approaches, tools, and evaluation data sets for the task exist, there is a distinct lack of support for languages other than English and scripts other than the Latin alphabet. A significant portion of the scientific literature that is thereby excluded consists of publications written in Cyrillic script languages. To address this problem, we introduce a new multilingual and multidisciplinary data set of over 100,000 labeled reference strings. The data set covers multiple Cyrillic languages and contains over 700 manually labeled references, while the remaining are generated synthetically. With random samples of varying size of this data, we train multiple well performing sequence labeling BERT models and thus show the usability of our proposed data set. To this end, we showcase an implementation of a multilingual BERT model trained on the synthetic data and evaluated on the manually labeled references. Our model achieves an F1 score of 0.93 and thereby significantly outperforms a state-of-the-art model we retrain and evaluate on our data.

Download: Media:Cyrillic_Citation_Field_Extraction_SDU-AAAI2022.pdf

Verknüpfte Datasets

Cyrillic Script Publication Metadata Extraction


Web Science


Information Retrieval, Natürliche Sprachverarbeitung, Digitale Bibliotheken, Deep Learning