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We present an architecture for information extraction from text that augmentsan existing parser with a character-level neural network. The network istrained using a measure of consistency of extracted data with existingdatabases as a form of noisy supervision. Our architecture combines the abilityof constraint-based information extraction systems to easily incorporate domainknowledge and constraints with the ability of deep neural networks to leveragelarge amounts of data to learn complex features. Boosting the existing parser'sprecision, the system led to large improvements over a mature and highly tunedconstraint-based production information extraction system used at Bloomberg forfinancial language text.

Information Extraction with Character-level Neural Networks and Free Noisy Supervision