Representation Learning for Information Extraction from Form-like Documents
Bodhisattwa Prasad Majumder, Navneet Potti, Sandeep Tata, James Bradley Wendt, Qi Zhao, Marc Najork
Abstract
We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains but are also interpretable, as we show using loss cases.- Anthology ID:
- 2020.acl-main.580
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6495–6504
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.580
- DOI:
- 10.18653/v1/2020.acl-main.580
- Cite (ACL):
- Bodhisattwa Prasad Majumder, Navneet Potti, Sandeep Tata, James Bradley Wendt, Qi Zhao, and Marc Najork. 2020. Representation Learning for Information Extraction from Form-like Documents. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6495–6504, Online. Association for Computational Linguistics.
- Cite (Informal):
- Representation Learning for Information Extraction from Form-like Documents (Majumder et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.acl-main.580.pdf