6. A computer implemented method of routing and filtering documents to topics comprising the steps of: breaking down each document for routing into small portions of up to approximately 250 words in length; calculating importance values of each word in both topics and the small portions of the documents; determining real value numbers for each of the small portions of document to each topic based on the importance values; calculating the real value number for the selected document based on adding the real value numbers of the small portions of the selected document; routing each document according to their respective real value numbers to one or more topics; and sorting the routed documents at each topic.
6. A computer implemented method of routing and filtering documents to topics comprising the steps of: breaking down each document for routing into small portions of up to approximately 250 words in length; calculating importance values of each word in both topics and the small portions of the documents; determining real value numbers for each of the small portions of document to each topic based on the importance values; calculating the real value number for the selected document based on adding the real value numbers of the small portions of the selected document; routing each document according to their respective real value numbers to one or more topics; and sorting the routed documents at each topic. 9. A computer implemented method of routing and filtering documents to topics of claim 6, wherein the determining a real value number step further includes the steps of: (i) calculating a first importance value for each word in the selected portion; (ii) calculating a second importance value for each word in the query that matches a word in the selected portion; (iii) determining a probability value for each word in the query matching a semantic category; (iv) determining a probability value for each word in the selected portion matching a semantic category; (v) adjusting for each word in the query that does not exist in the selected portion; (vi) repeating steps (i) to (iv) for each adjusted word; (vii) calculating weights of a semantic component in the query based on the importance value, the probability value and frequency of the word in the selected portion; (viii) calculating weights of a semantic component in the selected portion based on the importance value, the probability value and frequency of word in the query; (ix) multiplying query component weights by selected portion component weights into products; and (x) adding the products together to represent the real-value number for the selected document; and repeating steps (i) to (x) for each additional document selected.