47. A system that executes software to automatically extract relational information from a corpus of text, without operator supervision and without regard to a domain of the information, comprising: (a) in a first module, a self-supervising learning function that learns from training data how to classify information that will be extracted from the corpus of text as being trustworthy or not trustworthy, the self-supervising learning function producing a classifier; (b) in a second module, an extractor that makes a pass through the corpus of text and extracts candidate tuples, each candidate tuple including a plurality of objects coupled by at least one possible relationship, wherein the extractor provides the candidate tuples by tagging at least a portion of words within the corpus of text with each tagged word's most probable part of speech, without parsing the corpus of text and without generating a parse tree, the classifier processing each candidate tuple to determine if the candidate tuple is trustworthy, producing a set of retained tuples comprising all of the candidate tuples that have been determined by the classifier to be trustworthy; and (c) in a third module, a redundancy-based assessor that assigns a probability to each retained tuple, the probability being indicative of a likelihood that the retained tuple is an actual instance of a relationship between the plurality of objects comprising the retained tuple.
49. The system of claim 47 , wherein the extractor processes the corpus of text to tag words included therein as a most probable part-of-speech and then employs a noun chunker to identify noun phrases from which the candidate tuples are extracted.