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Title: PolyU_CBS-CFA at the FinSBD task : sentence boundary detection of financial data with domain knowledge enhancement and bilingual training
Authors: Wan, M 
Xiang, R 
Chersoni, E 
Klyueva, N 
Ahrens, K 
Miao, B 
Broadstock, D 
Kang, J 
Yung, A 
Huang, CR 
Issue Date: Aug-2019
Source: Proceedings of the First Workshop on Financial Technology and Natural Language Processing, Macao, China, August 2019, p. 122-129
Abstract: Sentence Boundary Detection is a basic requirement in Natural Language Processing and remains a challenge to language processing for specific purposes especially with noisy source documents. In this paper, we deal with the processing of scanned financial prospectuses with a feature-oriented and knowledge-enriched approach. Feature engineering and knowledge enrichment are conducted with the participation of domain experts and for the detection of sentence boundaries in both English and French. Two versions of the detection system are implemented with a Random Forest Classifier and a Neural Network. We engineer a fused feature set of punctuation, digital number, capitalization, acronym, letter and POS tag for model fitting. For knowledge enhancement, we implement a rule-based validation by extracting a keyword dictionary from the out-of-vocabulary sequences in FinSBD’s datasets. Bilingual training on both English and French training sets are conducted to ensure the multilingual robustness of the system and to extend the relatively small training data. Without using any extra data, our system achieves fair results on both tracks in the shared task. Our results (English1 : F1-Mean = 0.835; French: F1-Mean = 0.86) as well as a post-task quick improvement with self-adaptive knowledge enhancement based on testing data demonstrate the effectiveness and robustness of bilingual training with multi-feature mining and knowledge enhancement for domainspecific SBD task.
Rights: ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (
The following publication Wan, M., Xiang, R., Chersoni, E., Klyueva, N., Ahrens, K., Miao, B., ... & Huang, C. R. (2019). PolyU_CBS-CFA at the FinSBD Task: Sentence Boundary Detection of Financial Data with Domain Knowledge Enhancement and Bilingual Training. In Proceedings of the First Workshop on Financial Technology and Natural Language Processing (pp. 122-129) is available at
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