Integrating Top-Down and Bottom-Up Strategies in a Text Processing System

INTEGRATING TOP-DOWN AND BOTTOM-UP STRATEGIES IN A TEXT PROCESSING SYSTEM Lisa F. Rau and Paul S° Jacobs Artificial Intelligence Branch GE Company, Corporate R&D Schenectady, NY 12301 Abstract The SCISOR. system is a computer program designed to scan naturally occurring texts in constrained do- mains, extract information, and answer questions about that information. The system currently reads newspapers stories in the domain of corporate merg- ers and acquisitions. The language analysis strategy used by SCISOR combines full syntactic (bottom- up) parsing and conceptual expectation-driven (top- down) parsing. Four knowledge sources, includ- ing syntactic and semantic information and domain knowledge, interact in a flexible manner. This in- tegration produces a more robust semantic analyzer designed to deal gracefully with gaps in ]exical and syntactic knowledge, transports easily to new do- mains, and facilitates the extraction of information from texts. INTRODUCTION The System for Conceptual Information Summarization, Organization and Retrieval (SCISOR) is an implemented system designed to extract information from naturally oc- curring texts in constrained domains. The derived infor- mation is stored in a conceptual knowledge base and re- trieved using a natural language analyzer and generator. Conceptual information extracted from texts has a number of advantages over other information-retrieval techniques [Rau, 1987a], in addition to allowing for the automatic generation of databases from texts. The integration of top-down, expectation driven pro- cessing, and bottom-up, language-driven parsing is impor- tant for text understanding. Bottom-up strategies identify surface linguistic relations in the input and produce con- ceptual structures from these relations. With the input "ACE made ACME an offer", a good "bottom-up" linguistic analyzer can identify the subject, verb, direct and indirect objects. It also can determine that ACME was the recipi- ent of an offer, rather than being made into an offer, as in "ACE made ACME a subsidiary". Top-down methods use extensive knowledge of the context of the input, practical constraints, and conceptual expectations based on previous events to fit new informa- tion into an existing framework. A good "top-down" an- alyzer might determine from "ACE made ACME an offer" that ACME is the target of a takover (which is not obvious from the language, since the offer could be for something that ACME owns), and relate the offer to other events (pre- vious rumors or competing offers). Bottom-up methods tend to produce more accurate parses and semantic interpretations, account for subtleties in linguistic expression, and detect inconsistencies and lexi- cal gaps. Top-down methods are more tolerant of unknown words or grammatical lapses, but are also more apt to de- rive erroneous interpretations, fail to detect inconsisten- cies between what is said and how it is interpreted, and often cannot produce any results when the text presents unusual or unexpected information. Integration of these two approaches can improve the depth and accuracy of the understanding process. SCISOR is unique in its integration of the bottom-up processing performed by its analyzer, TRUMP (TRans- portable Understanding Mechanism Package) [Jacobs, 1986], with other sources of information in the form of conceptual expectations. In …

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