1. A computer-implemented method for estimating an interest a reader has in a requested document, useful in association with a list of textual terms with advertising value, the method comprising: building a trained model comprising using linear regression to identify a scoring function based on textual terms and associated documents, wherein the scoring function is of the form: 
 o ( k,p )=α+Σβ i log f i ( k )+Σγ i log g i ( k,p ) wherein β i and γ i are comparative relevance attributes and α is a relevancy factor; wherein k is a term factor and p is a web document identifier, eliminating extraneous material from the requested document; comparing a plurality of textual terms of the requested document to the list of textual terms with the advertising value, wherein the advertising value is based upon a bid value associated with one or more textual terms of the list of textual terms by one or more advertisers and the textual terms also including non-contextual features; computing a significance level based on the trained model for each of the plurality of textual terms of the requested document found on the list of textual terms with advertising value according to the advertising value of each of the plurality of terms of the requested document and computing the significant level based on the non-contextual feature of the positions of the textual terms in the requested document; ranking each of the plurality of textual terms of the requested document found on the list of textual terms with advertising value according to the significance level of each of the plurality of textual terms of the requested document that are found on the list of textual terms with advertising value; selecting one or more of the plurality of ranked textual terms as keywords to search for one or more relevant advertisements; and displaying the one or more relevant advertisements along with the requested document.
1. A computer-implemented method for estimating an interest a reader has in a requested document, useful in association with a list of textual terms with advertising value, the method comprising: building a trained model comprising using linear regression to identify a scoring function based on textual terms and associated documents, wherein the scoring function is of the form: 
 o ( k,p )=α+Σβ i log f i ( k )+Σγ i log g i ( k,p ) wherein β i and γ i are comparative relevance attributes and α is a relevancy factor; wherein k is a term factor and p is a web document identifier, eliminating extraneous material from the requested document; comparing a plurality of textual terms of the requested document to the list of textual terms with the advertising value, wherein the advertising value is based upon a bid value associated with one or more textual terms of the list of textual terms by one or more advertisers and the textual terms also including non-contextual features; computing a significance level based on the trained model for each of the plurality of textual terms of the requested document found on the list of textual terms with advertising value according to the advertising value of each of the plurality of terms of the requested document and computing the significant level based on the non-contextual feature of the positions of the textual terms in the requested document; ranking each of the plurality of textual terms of the requested document found on the list of textual terms with advertising value according to the significance level of each of the plurality of textual terms of the requested document that are found on the list of textual terms with advertising value; selecting one or more of the plurality of ranked textual terms as keywords to search for one or more relevant advertisements; and displaying the one or more relevant advertisements along with the requested document. 7. The method of claim 1 wherein the plurality of textual terms of the requested document include individual non-alphabetic words.