Term weighting deals with evaluating the importance of a term with respect to a document. A simple way is to think of this is that the term that appears more in the documents is an important term, apart from the stop words. A score from 0-1 can be assigned to each document. A score is a measurement that shows how well the term or query is matched in the document. A score of 0 means that the term does not exist in the document. As the frequency of the term increases in the document, the score moves from 0 toward 1. So, for a given term X, the scores for three documents, d1, d2, and d3 are 0.2, 0.3, and 0.5, respectively, which means that the match in d3 is more important than d2 and d1 is least important for the overall score. The same applies for the zones as well. How to assign such a score or weight to the term requires learning from some training set or continuously running and updating the score for terms.
The real-time query will be in the form of free text...