Representation Learning for Information Extraction from Form-like Documents
Bodhisattwa Prasad Majumder | Navneet Potti | Sandeep Tata | James Bradley Wendt | Qi Zhao | Marc Najork
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
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.