A system for self-learning archival of electronic data may be provided. A binary classifier may identify a text segment of an electronic dataset in response to text of the electronic dataset being associated with indicators of a word model. A first multiclass classifier may generate a first classification set comprising respective statistical metrics for the datafield that each predefined identifier in a group of predefined identifiers is representative of the datafield. A second multiclass classifier may receive a context of the electronic dataset and generate a second classification set. A combination classifier may apply weight values to the first classification set and the second classification set and form a weighted classification set and select a predefined identifier as being representative of the datafield based on the weighted classification set. The processor may store, in a memory, a data record comprising an association between the predefined identifier and the datafield.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a processor; a binary classifier executable by the processor to identify a text segment of an electronic dataset in response to text of the electronic dataset being associated with indicators of a word model, the indicators comprising datafield indicators and non-datafield indicators, the binary classifier further executable by the processor to determine that the text is a datafield based on a comparison of the text segment with the indicators of the word model; a first multiclass classifier executable by the processor to receive the electronic dataset and calculate a first classification set comprising respective statistical metrics for the datafield that each predefined identifier in a group of predefined identifiers is representative of the datafield; a second multiclass classifier executable by the processor to receive a context of the electronic dataset and calculate a second classification set comprising respective statistical metrics for the context of the electronic dataset that each predefined identifier in the group of predefined identifiers is representative of the context; a combination classifier executable by the processor to apply weight values to the first classification set and the second classification set and calculate a weighted classification set, the combination classifier further executable by the processor to select a predefined identifier of the predefined identifiers as being representative of the datafield based on the weighted classification set; and the processor configured to store, in a memory, a data record comprising an association between the predefined identifier and the datafield. 2. The system of claim 1, wherein the binary classifier is further executable by the processor to: calculate a confidence of the text segment being a datafield based on a comparison of the text segments with the datafield indicators and the non-datafield indicators; and determine that the text segment qualifies as the datafield in response to the confidence being greater than a predefined threshold. 3. The system of claim 1, further comprising a feedback controller executable by the processor to: determine a validity indication of a text segment being qualified as the datafield; and communicate the validity indication to the binary classifier, the binary classifier responsive to the validity indication to update the word model with the text of the text segment. 4. The system of claim 3, wherein the binary classifier is further executable by the processor to add text of the text segment to the datafield indicators of the word model in response to the validity indication indicative of a correct qualification of the text segment as the datafield. 5. The system of claim 3, wherein the binary classifier is further executable by the processor to add text of the text segment to the non-datafield indicators of the word model in response to the validity indication indicative of an incorrect qualification of the text segment as the datafield. 6. The system of claim 1, where in the context of the electronic dataset is all of the text of the electronic dataset. 7. The system of claim 1, where in the context of the electronic dataset is metadata of the electronic dataset. 8. A method comprising: identifying text of an electronic dataset associated with indicators of a word model, the indicators of the word model comprising datafield indicators and non-datafield indicators; determining that the text comprises a datafield based a comparison of the text with the indicators of the word model; generating datafield association statistical metrics according to a first data model, the datafield association statistical metrics including respective statistical metrics of each predefined identifier in a group of predefined identifiers being representative of the datafield; generating whole-content association statistical metrics according to a second data model, the whole-content association statistical metrics including respective statistical metrics of each predefined identifier in a group of predefined identifiers being representative of all of the electronic dataset; generating metadata association statistical metrics according to a third data model, the metadata association statistical metrics including respective statistical metrics of each predefined identifier in a group of predefined identifiers being representative of metadata of the electronic dataset; calculating modified datafield statistical metrics by combination of the datafield association statistical metrics, the whole-content association statistical metrics, and the metadata association statistical metrics, each of the modified datafield statistical metrics corresponding to a modified statistical metric of a respective predefined identifier in the group of predefined identifiers being representative of the datafield; identifying an association between the datafield and a predefined identifier from within the group of predefined identifiers based on the modified datafield statistical metrics; and storing the association between the datafield and the predefined identifier in a memory. 9. The method of claim 8, wherein the step of identifying an association between the datafield and a predefined identifier further comprises identifying one of the modified datafield statistical metrics with a highest confidence and generating an association between the respective predefined identifier corresponding to the one of the modified datafield statistical metrics with the highest confidence and the datafield. 10. The method of claim 8, wherein the step of calculating the modified datafield statistical metrics further comprises applying weight values to the datafield association statistical metrics, the whole-content association statistical metrics, and the metadata association statistical metrics; and calculating a weighted combination of the datafield association statistical metrics, the whole-content association statistical metrics, and the metadata association statistical metrics based on the weight values, wherein the modified datafield statistical metrics comprises the weighted combination. 11. The method of claim 10, wherein the weight values are applied to the datafield association statistical metrics, the whole-content association statistical metrics, and the metadata association statistical metrics according to a weighting model. 12. The method of claim 11, further comprising: determining a validity indication of the association between the datafield and the predefined identifier; and improving the weighting model based on the validity indication of the association. 13. The method of claim 12, wherein the step of improving the weighting model comprises adjusting the weight values applied to the datafield association statistical metrics, the whole-content association statistical metrics, and the metadata association statistical metrics. 14. The method of claim 8, further comprising: receiving a rule indicative of valid associations between the predefined identifiers and datafields; determining a validity indication of the association between the datafield and the predefined identifier based on the rule; and updating the first data model, the second data model, and the third data model with the validity indication. 15. A computer readable storage medium comprising computer executable instructions, the computer executable instructions executable by a processor, the computer executable instructions comprising: instructions executable to identify text of an electronic dataset associated with datafield indicators of a word model, the datafield indicators comprising groups of characters; instructions executable to determine that the text is a datafield based on the datafield indicators; instructions executable to generate a first classification set comprising respective statistical metrics for the datafield that each predefined identifier in a group of predefined identifiers is representative of the datafield; instructions executable to generate a second classification set comprising respective statistical metrics for a context of the electronic dataset that each predefined identifier in the group of predefined identifiers is representative of the context. instructions executable to apply weights to the first classification set and the second classification set and form a weighted classification set; instructions executable to select a predefined identifier of the predefined identifiers as being representative of the datafield based on the weighted classification set; and instructions executable to store, in a memory, a data record comprising an association between the predefined identifier and the datafield. 16. The computer readable storage medium of claim 15, further comprising: instructions executable by the processor to identify text of an electronic dataset associated with non-datafield indicators of a word model, the non-datafield indicators comprising groups of character indicative of non-datafield text; and instructions executable by the processor to determine that the text is not a datafield based on the non-datafield indicators. 17. The computer readable storage medium of claim 15, wherein the groups of characters comprise a group of characters previously determined to be a datafield. 18. The computer readable storage medium of claim 15, further comprising: instructions executable by the processor to receive a rule indicative of correct associations between the predefined identifiers and datafields; instructions executable by the processor to determine a validity indication of the association between the predefined identifier and the datafield; and instructions executable by the processor to adjust the weights in response to the validity indication. 19. The computer readable storage medium of claim 15, further comprising: instructions executable by the processor to search an association history for a previous association of a previous datafield with a predefined identifier, the association history further including a previous validity indication of the previous association, wherein at least one of the respective statistical metrics of the first classification set are based on the previous validity indication of the previous association. 20. The computer readable storage medium of claim 19, further comprising: instructions executable by the processor to determine a validity indication of the association between the predefined identifier and the datafield; and instructions executable by the processor to update the association history with the validity indication of the association.