20. A system for labeling, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: identify a plurality of patterns in a plurality of documents, the plurality of patterns not covered by a set of validated contextual rules; (a) select a batch of documents of the plurality of documents according to a frequency of usage of patterns included in the batch of documents of the plurality of documents, where the frequency of usage is for the plurality of documents; (b) submit the batch of documents of the plurality of documents to a crowdsourcing community for labeling; (c) receive labels for the batch of documents of the plurality of documents; (d) generate proposed contextual rules based on labeled documents of the plurality of documents by: identifying one or more patterns in the labeled documents and a usage frequency for the one or more patterns identified; selecting a top N patterns of the one or more patterns having highest usage frequencies; if a label of the labeled documents has a high correspondence to a pattern of the top N patterns of the one or more patterns, generating a proposed contextual rule relating the pattern of the top N patterns of the one or more patterns to the label; and otherwise, generating a proposed contextual rule that includes the pattern of the top N patterns of the one or more patterns but no related label; (e) submit the proposed contextual rules to an analyst community for validation; (f) receive validation of a portion of the proposed contextual rules; and (g) add the portion validated of the proposed contextual rules to the set of validated contextual rules.
20. A system for labeling, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: identify a plurality of patterns in a plurality of documents, the plurality of patterns not covered by a set of validated contextual rules; (a) select a batch of documents of the plurality of documents according to a frequency of usage of patterns included in the batch of documents of the plurality of documents, where the frequency of usage is for the plurality of documents; (b) submit the batch of documents of the plurality of documents to a crowdsourcing community for labeling; (c) receive labels for the batch of documents of the plurality of documents; (d) generate proposed contextual rules based on labeled documents of the plurality of documents by: identifying one or more patterns in the labeled documents and a usage frequency for the one or more patterns identified; selecting a top N patterns of the one or more patterns having highest usage frequencies; if a label of the labeled documents has a high correspondence to a pattern of the top N patterns of the one or more patterns, generating a proposed contextual rule relating the pattern of the top N patterns of the one or more patterns to the label; and otherwise, generating a proposed contextual rule that includes the pattern of the top N patterns of the one or more patterns but no related label; (e) submit the proposed contextual rules to an analyst community for validation; (f) receive validation of a portion of the proposed contextual rules; and (g) add the portion validated of the proposed contextual rules to the set of validated contextual rules. 22. The system of claim 20 , wherein the executable and operational data are further effective to cause the one or more processors to apply the set of validated contextual rules to the plurality of documents by, for a current document of the plurality of documents: identifying one or more tokens in the current document; searching for the one or more tokens identified in an index relating words to rules; counting a number of hits for each applicable rule of applicable rules having a corresponding token in the one or more tokens identified; comparing the number of hits for the each applicable rule of the applicable rules; and labeling the current document according to those applicable rules having a pattern word count greater than or equal to the number of hits for the each applicable rule of the applicable rules.