Abstract: A system for extracting relational tuples from sentences is provided. The system includes a bootstrapper, an open pattern learner, and a pattern matcher. The bootstrapper generates training data by, for each of a plurality of seed tuples, identifying sentences of a corpus that contains the words of the seed tuple. The open pattern learner learns, from the seed tuples and sentence pairs, open patterns that encode ways in which relational tuples may be expressed in a sentence, The pattern matcher matches the open patterns to a dependency parse of a sentence, identifies base nodes of the dependency parse for the arguments and relation for the relational tuple that the open pattern encodes, and expands the arguments and relation of the relational tuple.

Abstract: Systems and methods for the decision-theoretic control and optimization of crowd-sources workflows utilize a computing device to map a workflow to complete a directive. The directive includes a utility function, and the workflow comprises an ordered task set. Decision points precede and follow each task in the task set, and each decision point may require (a) posting a call for workers to complete instances of tasks in the task set; (b) adjusting parameters of tasks in the task set; or (c) submitting an artifact generated by a worker as output. The computing device accesses a plurality of workers having capability parameters that describe the workers' respective abilities to complete tasks. The computing device implements the workflow by optimizing and/or selecting user-preferred choices at decision points according to the utility function and submits an artifact as output. The computing device may also implement a training phase to ascertain worker capability parameters.