Question: What ability does the architecture gain?
Evidence: We present an information extraction architecture that augments a candidate-generating parser with a deep neural network. The candidate-generating parser may leverage constraints. At the same time, the architecture gains the neural networks's ability to leverage large amounts of data to learn complex features that are tuned for the application at hand. Our method assumes the existence of a potentially noisy source of supervision INLINEFORM0 , e.g. via consistency checks of extracted data against existing databases, or via human interaction. This supervision is used to train the neural network.
Our extraction system has three advantages over earlier work on information extraction with deep neural networks BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 :
Our system leverages “free” data to train a deep neural network, and does not require large-scale manual annotation. The network is trained with noisy supervision provided by measures of consistency with existing databases (e.g. an extraction ts_tick_abs (US_Unemployment, 49%) would be implausible given recent US employment history). With slight modifications, our pipeline could be trained with supervision from human interaction, such as clicks on online advertisements. Learning without explicit annotations is critical in applications where large-scale manual annotation would be prohibitively expensive.
If an extractor for the given application has already been built, the neural network boosts its accuracy without the need to re-engineer or discard the existing solution. Even for new systems, the decoupling of candidate-generation and the neural network offers advantages: the candidate-generating parser can easily enforce contraints that would be difficult to support in an algorithm relying entirely on a neural network. Note that, in particular, a carefully engineered candidate-generating parser enforces constraints intelligently, and can in many instances eliminate the need to evaluate computationally expensive constraints, e.g. API calls.
We encode the candidate-generating parser's document annotations character-by-character into vectors INLINEFORM0 that also include a one-hot encoding of the character itself. We believe that this encoding makes it easier for the network to learn character-level characteristics of the entities in the semantic relation. Moreover, our encoding lends itself well to processing both by recurrent architectures (processing character-by-character input vectors INLINEFORM1 ) and convolutional architectures (performing 1D convolutions over an input matrix whose columns are vectors INLINEFORM2 ).
In a production setting, the neural architecture presented here reduced the number of false positive extractions in financial information extraction application by INLINEFORM0 relative to a mature system developed over the course of several years.
At the same time, the architecture gains the neural networks's ability to leverage large amounts of data to learn complex features that are tuned for the application at hand.
Answer: The neural networks's ability to leverage large amounts of data to learn complex features that are tuned for the application at hand.