8. A method according to claim 6 , wherein: identifying at least two second tokens includes identifying a second token preceding the first token and a third token following the first token; determining a second PMM for each of the at least two second tokens from the PMM database includes determining the second PMM for the second token from the PMM database and determining a third PMM for the third token from the PMM database; using a proximity function to modify each of the second PMMs for each of the at least two second tokens includes using the proximity function to modify the second PMM for the second token and to modify the third PMM for the third token, the proximity function factoring in the first token and the distance between the first token and the second token and third token; and generating a RMM for the first token includes generating the RMM for the first token, the RMM factoring in the first PMM for the first token, the modified PMM for the second token, and the modified PMM for the third token.
6. A method, comprising: receiving a content at a server over a network; identifying a first token in the content using the server; determining a first probable measure of meaning (PMM) for the first token from a PMM database stored on the server; identifying at least two second tokens; determining a second PMM for each of the at least two second tokens from the PMM database stored on the server; using a proximity function (PF) to modify each of the second PMMs for each of the at least two second tokens, the proximity function factoring in the first token and a distance between the first token and each of the at least two second tokens; and generating a relative measure of meaning (RMM) for the first token, the RMM factoring in the first PMM for the first token and each of the modified PMMs for each of the at least two second tokens, according to R ⁢ ⁢ M ⁢ ⁢ M ⁡ ( token n ) = P ⁢ ⁢ M ⁢ ⁢ M ⁢ ⁢ ( token n ) ⨯ Mean d i = - d ⁡ ( P ⁢ ⁢ M ⁢ ⁢ M ⁡ ( token n - i ) * P ⁢ ⁢ F ⁡ ( token n , token n - i , i ) ) .