Abstract: A method for automated mapping of key terms to documents. Preferably a feature vector is generated for the key terms. Preferably such feature vectors are automatically generated by analyzing a corpus, but may optionally be generated manually, or using a combination of automated and manual processes. Next, preferably such feature vectors are weighted. Such weighting may optionally be performed manually, but more preferably is performed automatically. Next, a feature vector is optionally and preferably generated for the document, which preferably includes words and phrases that were extracted from the document but may optionally include words and phrases that do not appear in the document, such as synonyms and related. Each element in the document feature vector is preferably weighted. The feature vectors of the key terms and the feature vectors of the documents are compared, in order to produce relevancy scores, which are used to produce mapping between documents and key terms.

Abstract: The present invention provides methods and systems for automated mapping of advertisements to web pages. It provides the ability to provide target advertising and it provides the ability to sell key terms related to a particular advertisement. The invention also provides the ability to suggest key terms to potential purchasers based upon a content of the advertisement or based upon some other related information provided by the purchaser. The present invention also provides the ability to map the advertisements based on categories of semantically unrelated words and phrases. The method for mapping the advertisement to the web page includes analyzing the web page to determine its content. The content is then compared to a list of key terms. If the comparison results in a match and the match includes a key term, which has been purchased by an advertiser, that advertiser's advertisement may be mapped to the web page.

Abstract: A method for automated mapping of key terms to documents. Preferably a feature vector is generated for the key terms. Preferably such feature vectors are automatically generated by analyzing a corpus, but may optionally be generated manually, or using a combination of automated and manual processes. Next, preferably such feature vectors are weighted. Such weighting may optionally be performed manually, but more preferably is performed automatically. Next, a feature vector is optionally and preferably generated for the document, which preferably includes words and phrases that were extracted from the document but may optionally include words and phrases that do not appear in the document, such as synonyms and related. Each element in the document feature vector is preferably weighted. The feature vectors of the key terms and the feature vectors of the documents are compared, in order to produce relevancy scores, which are used to produce mapping between documents and key terms.