1. A method for identifying the language of a document in which a computer document is written, comprising the steps of: comparing a plurality of words from the document to a word list associated with a candidate language, wherein words in the word list are a selection of a small number of the most frequently used words in the candidate language; accumulating a count of matches between words in the document and words in the word list for each word in the word list to produce a sample count for each word in the word list; correlating the sample count to a reference count for each word in the word list for the candidate language to produce a correlation score for the candidate language, wherein the correlation score is a statistical measure of a collective strength of association between the sample counts and reference counts; and identifying the language of the document based on the correlation score.
1. A method for identifying the language of a document in which a computer document is written, comprising the steps of: comparing a plurality of words from the document to a word list associated with a candidate language, wherein words in the word list are a selection of a small number of the most frequently used words in the candidate language; accumulating a count of matches between words in the document and words in the word list for each word in the word list to produce a sample count for each word in the word list; correlating the sample count to a reference count for each word in the word list for the candidate language to produce a correlation score for the candidate language, wherein the correlation score is a statistical measure of a collective strength of association between the sample counts and reference counts; and identifying the language of the document based on the correlation score. 2. The method as recited in claim 1 wherein the comparing, accumulating, correlating and identifying steps are practiced for a plurality of candidate languages each with a respective word list and a respective reference count for each word in the word list and the language of the document is identified as the candidate language having a reference count which generates a highest correlation score.