TY - GEN
T1 - Spatial Dependency Parsing for Semi-Structured Document Information Extraction
AU - Hwang, Wonseok
AU - Yim, Jinyeong
AU - Park, Seunghyun
AU - Yang, Sohee
AU - Seo, Minjoon
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics
PY - 2021
Y1 - 2021
N2 - Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories. However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and (2) it is not suitable for highly structured information, which are nevertheless frequently observed in real-world document images. To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text tokens in the documents. Under this setup, we then propose SPADEs (SPAtial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers in the documents in an end-to-end manner. We evaluate it on various kinds of documents such as receipts, name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger.
AB - Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories. However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and (2) it is not suitable for highly structured information, which are nevertheless frequently observed in real-world document images. To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text tokens in the documents. Under this setup, we then propose SPADEs (SPAtial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers in the documents in an end-to-end manner. We evaluate it on various kinds of documents such as receipts, name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger.
UR - http://www.scopus.com/inward/record.url?scp=85113481218&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85113481218
T3 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
SP - 330
EP - 343
BT - Findings of the Association for Computational Linguistics
A2 - Zong, Chengqing
A2 - Xia, Fei
A2 - Li, Wenjie
A2 - Navigli, Roberto
PB - Association for Computational Linguistics (ACL)
T2 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Y2 - 1 August 2021 through 6 August 2021
ER -