2016/12/13 by Philipp Meerkamp, Zhengyi Zhou, Meerkamp, Philipp +1 · 1 voice
Computer Science · #Handwritten Text Recognition Techniques #Mathematics, Computing, and Information Processing #Natural Language Processing Techniques #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1612.04118
openalex publication_date 2016/12/13 · openalex created_date 2022/09/01 · openalex updated_date 2026/07/28
We present an architecture for information extraction from text that augments an existing parser with a character-level neural network. The network is trained using a measure of consistency of extracted data with existing databases as a form of noisy supervision. Our architecture combines the ability of constraint-based information extraction systems to easily incorporate domain knowledge and constraints with the ability of deep neural networks to leverage large amounts of data to learn complex features. Boosting the existing parser's precision, the system led to large improvements over a mature and highly tuned constraint-based production information extraction system used at Bloomberg for financial language text.