1. A method for identifying spam in an email, the method comprising: (a) normalizing an email text morphologically and identifying unique words in the email text; (b) filtering words from the email text, including filtering multi-symbol meaningless human-language words and noise human-language words; (c) determining a number of occurrences of each unique word in the email text; (d) creating a unique numerical identifier for each unique word, the identifier being based on a numerical value corresponding to the unique word; (e) assigning an unique numerical identifier to each unique word in the email text; (f) generating a lexical vector of the email text as a plurality of the assigned identifiers and a frequency of occurrence of each corresponding unique word in the email text; (g) generating a histogram of the lexical vector for each unique numerical identifier of each corresponding unique word in the email text; (h) performing only a single comparison of the histogram of the lexical vector to histograms of lexical vectors of known spam texts; and (i) determining if the email text is spam based on a result of comparison of the histograms.
1. A method for identifying spam in an email, the method comprising: (a) normalizing an email text morphologically and identifying unique words in the email text; (b) filtering words from the email text, including filtering multi-symbol meaningless human-language words and noise human-language words; (c) determining a number of occurrences of each unique word in the email text; (d) creating a unique numerical identifier for each unique word, the identifier being based on a numerical value corresponding to the unique word; (e) assigning an unique numerical identifier to each unique word in the email text; (f) generating a lexical vector of the email text as a plurality of the assigned identifiers and a frequency of occurrence of each corresponding unique word in the email text; (g) generating a histogram of the lexical vector for each unique numerical identifier of each corresponding unique word in the email text; (h) performing only a single comparison of the histogram of the lexical vector to histograms of lexical vectors of known spam texts; and (i) determining if the email text is spam based on a result of comparison of the histograms. 8. The method of claim 1 , wherein the lexical vectors of the known spam texts are stored in a lexical vector database.