Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-program product tangibly embodied in a non-transitory computer-readable storage medium having instructions stored thereon, the instructions executable to cause a data processing apparatus to perform operations including: receiving a set of data files; determining a plurality of rule sets based on the set of data files, wherein determining a rule set includes applying a context to a rule set; comparing two rule sets of the plurality of rule sets, wherein comparing includes identifying a dimensional differentiator for the two rules sets and a common node for the two rule sets, wherein the dimensional differentiator is only associated with one context, and wherein the common node is associated with both contexts; generating a canonical rule set using the compared rule sets, the dimensional differentiator, and the common node; and presenting the canonical rule set, wherein presenting includes visually distinguishing the dimensional differentiator from the common node. 2. The computer-program product of claim 1, wherein the operations further include: determining a cross-corpus rule set based on the set of data files, wherein generating the canonical rule set includes generating the canonical rule set additionally using the cross-corpus rule set. 3. The computer-program product of claim 2, wherein the common node is additionally associated with the cross-corpus rule set. 4. The computer-program product of claim 1, wherein presenting the canonical rule set includes visually presenting the canonical rule set, and wherein the dimensional differentiator is visually distinguishable from the common node. 5. The computer-program product of claim 1, wherein presenting the canonical rule set includes distinguishing the dimensional differentiator by context. 6. The computer-program product of claim 1, wherein presenting the canonical rule set includes presenting the common node as being associated with both contexts. 7. The computer-program product of claim 1, wherein applying the context to the rule set includes generating the rule set based on a sub-domain of the set of data files associated with the context. 8. A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more processors to perform operations including: receiving a set of data files; determining a plurality of rule sets based on the set of data files, wherein determining a rule set includes applying a context to a rule set; comparing two rule sets of the plurality of rule sets, wherein comparing includes identifying a dimensional differentiator for the two rules sets and a common node for the two rule sets, wherein the dimensional differentiator is only associated with one context, and wherein the common node is associated with both contexts; generating a canonical rule set using the compared rule sets, the dimensional differentiator, and the common node; and presenting the canonical rule set, wherein presenting includes visually distinguishing the dimensional differentiator from the common node. 9. The system of claim 8, wherein the operations further include: determining a cross-corpus rule set based on the set of data files, wherein generating the canonical rule set includes generating the canonical rule set additionally using the cross-corpus rule set. 10. The system of claim 9, wherein the common node is additionally associated with the cross-corpus rule set. 11. The system of claim 8, wherein presenting the canonical rule set includes visually presenting the canonical rule set, and wherein the dimensional differentiator is visually distinguishable from the common node. 12. The system of claim 8, wherein presenting the canonical rule set includes distinguishing the dimensional differentiator by context. 13. The system of claim 8, wherein presenting the canonical rule set includes presenting the common node as being associated with both contexts. 14. The system of claim 8, wherein applying the context to the rule set includes generating the rule set based on a sub-domain of the set of data files associated with the context. 15. A computer-implemented method, comprising: receiving a set of data files; determining a plurality of rule sets based on the set of data files, wherein determining a rule set includes applying a context to a rule set; comparing two rule sets of the plurality of rule sets, wherein comparing includes identifying a dimensional differentiator for the two rules sets and a common node for the two rule sets, wherein the dimensional differentiator is only associated with one context, and wherein the common node is associated with both contexts; generating a canonical rule set using the compared rule sets, the dimensional differentiator, and the common node; and presenting the canonical rule set, wherein presenting includes visually distinguishing the dimensional differentiator from the common node. 16. The method of claim 15, further comprising: determining a cross-corpus rule set based on the set of data files, wherein generating the canonical rule set includes generating the canonical rule set additionally using the cross-corpus rule set. 17. The method of claim 15, wherein presenting the canonical rule set includes visually presenting the canonical rule set, and wherein the dimensional differentiator is visually distinguishable from the common node. 18. The method of claim 15, wherein presenting the canonical rule set includes distinguishing the dimensional differentiator by context. 19. The method of claim 15, wherein presenting the canonical rule set includes presenting the common node as being associated with both contexts. 20. The method of claim 15, wherein applying the context to the rule set includes generating the rule set based on a sub-domain of the set of data files associated with the context.