IMPROVING DOCUMENT RETRIEVAL WITH NATURAL LANGUAGE UNDERSTANDING TECHNIQUES

THE GOAL OF THIS PROPOSED PROGRAM IS TO DEMONSTRATE THAT THE INCORPORATION OF ADVANCED NATURAL LANGUAGE PROCESSING TECHNIQUES INTO A DOCUMENT RETRIEVAL SYSTEM WILL SIGNIFICANTLY IMPROVE PERFORMANCE BOTH IN TERMS OF THE NUMBER OF RELEVANT DOCUMENTS RETRIEVED AND IN THE PRECISION WITH WHICH RELEVANT DOCUMENTS ARE SELECTED. EXISTING SYSTEMS ARE BASED ON KEYWORD SEARCHING ENHANCED BY STATISTICAL AND COMPUTATIONAL METHODS WITH SOME HUMAN INTERVENTION, USUALLY IN THE DOCUMENT INDEXING PROCESS. WHILE THESE METHODS HAVE BEEN PUSHED TO ACHIEVE A REASONABLE LEVEL OF SUCCESS, RESEARCHERS HAVE VERY NEARLY REACHED THE LIMITS OF SUCH METHODS AND LABOR-INTENSIVE HUMAN INTERVENTION BECOMES LESS AND LESS FEASIBLE WITH THE GROWING INFORMATION EXPLOSION. ADVANCED NATURAL LANGUAGE PROCESSING TECHNIQUES OFFER THE BEST HOPE OF BREAKING OUT OF THESE LIMITS BECAUSE THEY WOULD BRING TO THE RETRIEVAL TASK HUMAN-LIKE UNDERSTANDING CAPABILITIES. THESE INCLUDE DISAMBIGUATION OF WORD SENSES, IDENTIFICATION OF MAJOR PARTICIPANTS IN REPORTED EVENTS, ASSIGNMENT OF TEMPORAL AND CAUSAL RELATIONS BETWEEN REPORTED EVENTS, AND IDENTIFICATION OF TOPICS AND SUBTOPICS. THE TARGET SYSTEM WILL BE MORE PRECIE BECAUSE IT WILL LEVERAGE WHAT IS BEST IN EXISTING DOCUMENT RETRIEVAL TECHNOLOGY BY INTEGRATING IT WITH DEEP NATURAL LANGUAGE UNDERSTANDING BUILT ON NAIVE SEMANTIC KNOWLEDGE OF THE WORLD.