Most commercial online retrieval systems are not designed to service end users and, therefore, have often built "front-ends" to their systems specifically to serve the end-user market. These front-ends have not been well accepted, mostly because the underlying systems are still difficult for end users to use successfully in searching. New techniques, based on statistical methods, that allow natural language input and return lists of records in order of likely relevance, have long been available from research laboratories. This article presents four prototype implementations of these statistical retrieval systems that demonstrate their potential as powerful and easily used retrieval systems able to service all users.
