Abstract: A system, method, and/or computer program product for automatic topic identification using a hypertext corpus may include a) receiving a content document(s); b) identifying or lexically scoring candidate topic(s) in the received content document based on label(s) used in a corpus to link to or relate to the candidate topics; c) evaluating or semantically scoring the candidate topic(s) of the received document based on a relationship between two or more candidate topics in the corpus; and d) weighting candidate topics for relevance based on algorithmic or statistical analysis of links or relationships in the corpus.

Abstract: A system, method, and/or computer program product for automatic topic identification using a hypertext corpus may include a) receiving a content document(s); b) identifying or lexically scoring candidate topic(s) in the received content document based on label(s) used in a corpus to link to or relate to the candidate topics; c) evaluating or semantically scoring the candidate topic(s) of the received document based on a relationship between two or more candidate topics in the corpus; and d) weighting candidate topics for relevance based on algorithmic or statistical analysis of links or relationships in the corpus.

Abstract: A system, method, and/or computer program product for automatic topic identification using a hypertext corpus may include a) receiving a content document(s); b) identifying or lexically scoring candidate topic(s) in the received content document based on label(s) used in a corpus to link to or relate to the candidate topics; c) evaluating or semantically scoring the candidate topic(s) of the received document based on a relationship between two or more candidate topics in the corpus; and d) weighting candidate topics for relevance based on algorithmic or statistical analysis of links or relationships in the corpus.

Abstract: A system, method, and/or computer program product for automatic topic identification using a hypertext corpus may include a) receiving a content document(s); b) identifying or lexically scoring candidate topic(s) in the received content document based on label(s) used in a corpus to link to or relate to the candidate topics; c) evaluating or semantically scoring the candidate topic(s) of the received document based on a relationship between two or more candidate topics in the corpus; and d) weighting candidate topics for relevance based on algorithmic or statistical analysis of links or relationships in the corpus.