1. A system for training an information extraction model, the information extraction model capable of allowing a user to request information to be extracted from a user-supplied input, the system comprising: (a) a baseline training module implemented as computer readable instructions stored on one or more computer-readable storage devices and executable on one or more computers, the baseline training module programmed to: (i) receive an annotated corpus of text, the annotated corpus of text being associated with classifications and tags for each word in the annotated corpus of text and an annotated path for the classifications and tags; (ii) pass each sentence contained within the annotated corpus of text to an evaluation module, the evaluation module determining classifications and tags for each word within the annotated corpus of text based on data stored in a feature weight table and in a cluster tree, the evaluation module predicting a path for the classifications and tags; (iii) compare the predicted path with the annotated path; and (iv) if the predicted path and the annotated path match, the baseline training module skips to the next sentence, and if the predicted path and the annotated path do not match, the baseline training module adjusts the feature weights in the feature weight table corresponding to the unmatched paths; and (b) an active training module implemented as computer readable instructions stored on one or more computer-readable storage devices and executable on one or more computers, the active training module programmed to: (i) receive an unannotated corpus of text; (ii) pass each sentence contained within the unannotated corpus of text to the evaluation module, the evaluation module determining classifications and tags for each word within the unannotated corpus of text based on data stored in the feature weight table and in the cluster tree; (iii) calculate a confidence score indicating the confidence the evaluation module has in its tagging decisions for each sentence; (iv) select a subset of the sentences for review based on the confidence scores; (v) send the subset of sentences to at least one active learning user interface for review; (vi) receive, from the at least one active learning user interface, instructions to update the feature weights in the feature weight table based on the review; and (vii) update the feature weights in the feature weight table with the received instructions.
1. A system for training an information extraction model, the information extraction model capable of allowing a user to request information to be extracted from a user-supplied input, the system comprising: (a) a baseline training module implemented as computer readable instructions stored on one or more computer-readable storage devices and executable on one or more computers, the baseline training module programmed to: (i) receive an annotated corpus of text, the annotated corpus of text being associated with classifications and tags for each word in the annotated corpus of text and an annotated path for the classifications and tags; (ii) pass each sentence contained within the annotated corpus of text to an evaluation module, the evaluation module determining classifications and tags for each word within the annotated corpus of text based on data stored in a feature weight table and in a cluster tree, the evaluation module predicting a path for the classifications and tags; (iii) compare the predicted path with the annotated path; and (iv) if the predicted path and the annotated path match, the baseline training module skips to the next sentence, and if the predicted path and the annotated path do not match, the baseline training module adjusts the feature weights in the feature weight table corresponding to the unmatched paths; and (b) an active training module implemented as computer readable instructions stored on one or more computer-readable storage devices and executable on one or more computers, the active training module programmed to: (i) receive an unannotated corpus of text; (ii) pass each sentence contained within the unannotated corpus of text to the evaluation module, the evaluation module determining classifications and tags for each word within the unannotated corpus of text based on data stored in the feature weight table and in the cluster tree; (iii) calculate a confidence score indicating the confidence the evaluation module has in its tagging decisions for each sentence; (iv) select a subset of the sentences for review based on the confidence scores; (v) send the subset of sentences to at least one active learning user interface for review; (vi) receive, from the at least one active learning user interface, instructions to update the feature weights in the feature weight table based on the review; and (vii) update the feature weights in the feature weight table with the received instructions. 12. The system for training an information extraction model of claim 1 wherein the active training module selects all sentences having confidence scores below a threshold value.