Chilecompra is the Chilean governmental agency in charge of Chile's Public Procurement System. One of its main current problems is that around 54% (supply indicator) of the public procurements that are submitted get less than three bids, which is the minimum required so that the procurement process may be free of technical, legal and ethical questionings. This article presents a method based on Natural Language Processing and Semantic Analysis in order to automatically identify prospective bidding candidates from a historical application dataset with a software tool which implements this method. The results are proven by using a dataset of procurement data and historical applications, biddings and adjudications of the suppliers. We compared the number of suppliers that applied for a tender and the number of prospective bidders identified by this method, and the domain procurement experts proved that the identified bidders were relevant to the new procurement.