1. A computer-implemented method for providing default hierarchical training for social indexing, comprising: maintaining articles of digital information for social indexing; specifying a hierarchically-structured tree of topics, which each comprise a label comprising one or more words; identifying hard constraints based on the labels comprised in the topic tree and the topic tree's hierarchical structure, and defining the hard constraints to include immutable rules comprising at least one of: requiring that a topic model comprises a single term comprised from a label that is duplicated within the topic tree; requiring that a topic model includes no term from the label for the topic to which the topic model belongs; and when the label is duplicated within the topic tree, requiring that a topic model includes no term from the label of a parent topic for the topic to which the topic model belongs; for each topic in the topic tree, creating a topic model subject to the hard constraints, the topic model comprising a finite state pattern that comprises a pattern evaluable against the articles; evaluating the topic models for the topic tree against the hard constraints and disfavoring those topic models that violate one or more of the immutable rules; and identifying for each topic, the topic model, which best satisfies the constraints.
8. A method according to claim 1 , wherein the at least one term comprises at least one of one of the terms in the labels, an n-gram comprised of a plurality of the terms in the labels, and a group of the terms in the labels.