Session-history-based recency-biased natural language document search

摘要

A method and apparatus make keyword selection and/or weighting as a function of a session history of user input in order to answer queries submitted by the user to a computer system by providing answers based on stored documents. The aim is to find the best answers by matching stored natural language documents both to the most recent query and to the most latest query in a context that captures the recent history interaction. To do this, answers are matched against a set of keywords extracted from the most recent query as well as a set of keywords extracted from those queries received since the last topic switch was detected. A central feature of the method is for the computer system implementing this method to maintain a session history for each user session history. Keywords are extracted from each query by a system implementing this method. A graded keyword list is a list of keywords paired with ages, which are indicators of how long ago in the session the user employed this keyword in a query. Graded keyword lists are maintained in the session history so the system can assign weights to keywords, with more recently received keywords being assigned higher weights than keywords with comparatively greater ages. The weights assigned to keywords are used in computing scores that indicate closeness of match of a document to a list of keywords.