by how much did the system improve?
By more than 90%
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.
Unstructured textual data is abundant in the financial domain (see e.g. Figure FIGREF2 ). This information is by definition not in a format that lends itself to immediate processing. Hence, information extraction is an essential step in business applications that require fast, accurate, and low-cost information processing. In the financial domain, these applications include the creation of time series databases for macroeconomic forecasting or financial analysis, as well as the real-time extraction of time series data to inform algorithmic trading strategies. Bloomberg has had information extraction systems for financial language text for nearly a decade. To meet the application domain's high accuracy requirements, marrying constraints with statistical models is often beneficial, see e.g. BIBREF0 , BIBREF1 . Many quantities appearing in information extraction problems are by definition constrained in the numerical values they can assume (e.g. unemployment numbers cannot be negative numbers, while changes in unemployment numbers can be negative). The inclusion of such constraints may significantly boost data efficiency. Constraints can be complex in nature, and may involve multiple entities belonging to an extraction candidate generated by the parser. At Bloomberg, we found the system for information extraction described in this paper especially useful to extract time series (TS) data. As an example, consider numerical relations of the form ts_tick_abs (TS symbol, numerical value), e.g. ts_tick_abs (US_Unemployment, 4.9%), or ts_tick_rel (TS symbol, change in num. value), e.g. ts_tick_abs (US_Unemployment, -0.2%).