27. A computerized data processing analysis system of improving accuracy of computerized topic identification comprising: at least one memory; and at least one topic identification computer processor coupled to said at least one memory, said at least one topic identification computer processor configured to: execute a domain independent, language independent, computer processor automated topic identification analysis module, wherein said at least one topic identification computer processor is configured to: derive a lexicon from at least one hypertext corpus data set to associate at least one term with at least one topic, wherein said at least one term comprises at least one word, wherein at least one sense is derived from at least one hypertext link of the at least one hypertext corpus data set, and is associated with each of said at least one term, wherein each of said at least one sense refers to a single topic of said at least one term, wherein said at least one topic referred to by said at least one sense is to be used as a candidate topic at runtime, wherein a prior probability is associated with each of said at least one sense of said each said term, wherein each said prior probability is a fraction of occurrences of a given one of said at least one term as a relationship comprising a hypertext link that links to said single topic from said each of said at least one sense; receive at least one content document; search for at least one term from the lexicon derived from the at least one hypertext corpus data set, and find the at least one term from the lexicon that appears in the at least one content document to determine at least one candidate topic of the at least one content document; lexically score each of said at least one candidate topic found that appears in the at least one content document based on the at least one term found in said search of the at least one content document to obtain a lexical score for each of said at least one candidate topic, and accumulate said lexical score for each of said at least one candidate topic for each occurrence in the at least one content document of a term, wherein the term has an associated sense, wherein the associated sense refers to said each said candidate topic, lexically score based on: a number of occurrences of the at least one term found in the at least one content document, a weighting factor representing a relative importance of the at least one term found, and the prior probability of the sense of the at least one term found; and semantically score the at least one candidate topic found in the at least one content document, based on a degree to which any plurality of candidate topics are semantically related to each other comprising: wherein said at least one topic identification computer processor is configured to: quantify a semantic relatedness score of said any plurality of topics of the at least one hypertext corpus data set, and wherein said quantifying of the semantic relatedness score is based on theoretical information content of co-adjacent links of a graph representation of the at least one hypertext corpus data set; evaluating the semantic relatedness score during quantifying, by the at least one computer processor, comprising evaluating the theoretical information content of an edge of said graph representation of the at least one hypertext corpus data set, wherein a vertex of said graph representation of the at least one hypertext corpus data set represents one of said plurality of topics, and an edge of said co-adjacent edges represents a relationship between two topics; wherein the theoretical information content of an edge is based on information theory and comprises at least one of: A. self-information, or B. surprisal ii. semantically score based on said lexical score of said each of said at least one candidate topic found appearing in the at least one content document, wherein said lexical score of said each of said at least one candidate topic was determined from the at least one term of said lexicon derived from the hypertext corpus data set, and based on said semantic relatedness score from said quantifying.
27. A computerized data processing analysis system of improving accuracy of computerized topic identification comprising: at least one memory; and at least one topic identification computer processor coupled to said at least one memory, said at least one topic identification computer processor configured to: execute a domain independent, language independent, computer processor automated topic identification analysis module, wherein said at least one topic identification computer processor is configured to: derive a lexicon from at least one hypertext corpus data set to associate at least one term with at least one topic, wherein said at least one term comprises at least one word, wherein at least one sense is derived from at least one hypertext link of the at least one hypertext corpus data set, and is associated with each of said at least one term, wherein each of said at least one sense refers to a single topic of said at least one term, wherein said at least one topic referred to by said at least one sense is to be used as a candidate topic at runtime, wherein a prior probability is associated with each of said at least one sense of said each said term, wherein each said prior probability is a fraction of occurrences of a given one of said at least one term as a relationship comprising a hypertext link that links to said single topic from said each of said at least one sense; receive at least one content document; search for at least one term from the lexicon derived from the at least one hypertext corpus data set, and find the at least one term from the lexicon that appears in the at least one content document to determine at least one candidate topic of the at least one content document; lexically score each of said at least one candidate topic found that appears in the at least one content document based on the at least one term found in said search of the at least one content document to obtain a lexical score for each of said at least one candidate topic, and accumulate said lexical score for each of said at least one candidate topic for each occurrence in the at least one content document of a term, wherein the term has an associated sense, wherein the associated sense refers to said each said candidate topic, lexically score based on: a number of occurrences of the at least one term found in the at least one content document, a weighting factor representing a relative importance of the at least one term found, and the prior probability of the sense of the at least one term found; and semantically score the at least one candidate topic found in the at least one content document, based on a degree to which any plurality of candidate topics are semantically related to each other comprising: wherein said at least one topic identification computer processor is configured to: quantify a semantic relatedness score of said any plurality of topics of the at least one hypertext corpus data set, and wherein said quantifying of the semantic relatedness score is based on theoretical information content of co-adjacent links of a graph representation of the at least one hypertext corpus data set; evaluating the semantic relatedness score during quantifying, by the at least one computer processor, comprising evaluating the theoretical information content of an edge of said graph representation of the at least one hypertext corpus data set, wherein a vertex of said graph representation of the at least one hypertext corpus data set represents one of said plurality of topics, and an edge of said co-adjacent edges represents a relationship between two topics; wherein the theoretical information content of an edge is based on information theory and comprises at least one of: A. self-information, or B. surprisal ii. semantically score based on said lexical score of said each of said at least one candidate topic found appearing in the at least one content document, wherein said lexical score of said each of said at least one candidate topic was determined from the at least one term of said lexicon derived from the hypertext corpus data set, and based on said semantic relatedness score from said quantifying. 28. The system according to claim 27 , wherein said at least one topic identification computer processor is configured to: weight said at least one candidate topic for relevance based on at least one of: algorithmic analysis of relatedness, or statistical analysis of relatedness.