1. A computer-implemented method comprising: a. receiving, by a template module of a computer, from a content database a corpus comprising a set of pre-segmented texts; b. creating, by the template module of the computer, a plurality of modified pre-segmented texts for the set of pre-segmented texts by: i. extracting, by the template module of the computer, a set of semantic terms for each pre-segmented text within the set of pre-segmented texts; and ii. applying, by the template module of the computer, at least one domain tag for each pre-segmented text within the set of pre-segmented texts; c. clustering, by the template module of the computer utilizing a k-means clustering technique, the plurality of modified pre-segmented texts into one or more conceptual units, the k-means clustering technique including: i. placing k points into the space represented by a set of modified pre-segmented texts that are being clustered, the k points representing initial group centroids; ii. assigning each of the set of modified pre-segmented texts to the group having the closest centroid; iii. when all of the set of modified pre-segmented texts have been assigned, recalculating the positions of the k centroids; and iv. repeating steps ii. and iii. until the centroids become stable; wherein each of the one or more conceptual units is represented as k clusters and is associated with one or more templates, wherein each of the one or more templates corresponds to one of the set of pre-segmented texts, and wherein a conceptual unit identifier is assigned to each modified pre-segmented text and pre-segmented text in the plurality of modified pre-segmented texts and set of pre-segmented texts respectively; d. determining a gold template; e. identifying a set of matching templates from the one or more templates within a given conceptual unit, the set of matching templates associated with the gold template; f. ranking the set of matching templates, a ranked set of matching templates being associated with the gold template; g. determining a set of model weights based on one or more ranked sets of matching templates and a set of ranking features; and h. storing the plurality of modified pre-segmented texts, the set of model weights, and the set of semantic terms in the content database.