DeepDive

By allowing users to build their system end-to-end, DeepDive allows users to focus on the portion of their system that most directly improves the quality of their application. By contrast, previous pipeline-based systems require developers to build extractors, integration code, and other components-without any clear idea of how their changes improv... e the quality of their data product. This simple insight is the key to how DeepDive systems produce higher quality data in less time. DeepDive-based systems are used by users without machine learning expertise in a number of domains from paleobiology to genomics to human trafficking; see our showcase for examples. DeepDive is a trained system that uses machine learning to cope with various forms of noise and imprecision. DeepDive is designed to make it easy for users to train the system through low-level feedback via the Mindtagger interface and rich, structured domain knowledge via rules. DeepDive wants to enable experts who do not have machine learning expertise. One of DeepDive's key technical innovations is the ability to solve statistical inference problems at massive scale. DeepDive differs from traditional systems in several ways: DeepDive asks the developer to think about features-not algorithms. In contrast, other machine learning systems require the developer think about which clustering algorithm, which classification algorithm, etc. to use. In DeepDive's joint inference based approach, the user only specifies the necessary signals or features. DeepDive systems can achieve high quality: PaleoDeepDive has higher quality than human volunteers in extracting complex knowledge in scientific domains and winning performance in entity relation extraction competitions. DeepDive is aware that data is often noisy and imprecise: names are misspelled, natural language is ambiguous, and humans make mistakes. Taking such imprecision into account, DeepDive computes calibrated probabilities for every assertion it makes. For example, if DeepDive produces a fact with ...Read More