Recovering Implicit Information

Reeoverlng Impl|clt Information RECOVERING IMPLICIT INFORMATION Martha S. Palmer, Deborah A. Dahl, Rebecca J. Schiffman, Lynette Hirschman, Marcia Linebarger, and John Dowding Research and Development Division SDC -- A Burroughs Company P.O Box 517 Paoli, PA 19301 USA ABSTRACT This paper describes the SDC PUNDIT, (Prolog UNDerstands Integrated Text), system for processing natural language messages. 1 PUNDIT , written in Prolog, is a highly modular system consisting of distinct syntactic, semantic and prag- matics components. Each component draws on one or more sets of data, includ- ing a lexicon, a broad-coverage grammar of English, semantic verb decomposi- tions, rules mapping between syntactic and semantic constituents, and a domain model. This paper discusses the communication between the syntactic, semantic and pragmatic modules that is necessary for making implicit linguistic informa- tion explicit. The key is letting syntax and semantics recognize missing linguis- tic entities as implicit entities, so that they can be labelled as such, and refer- ence resolution can be directed to find specific referents for the entities. In this way the task of making implicit linguistic information explicit becomes a subset of the tasks perfgrmed by reference resolution. The success of this approach is dependent on marking missing syntactic constituents as elided and missing semantic roles as ESSENTIAL so that reference resolution can know when to look for referents. I This work is supported in part by DARPA under contract N00014-85-C-0012, administered by the Office of Na- val Research. APPROVED FOR PUBLIC RELEASE, DISTRIBUTION UNLIMITED. 96 Reeoverlng Implicit Information 1. Introduction This paper describes tile SDC PUNDIT 2 system for processing natural language messages. PUNDIT, written in Prolog, is a highly modular system consisting of distinct syntactic, semantic and pragmatics components. Each component draws on one or more sets of data, including a lexicon, a broad- coverage grammar of English, semantic verb decompositions, rules mapping between syntactic and semantic constituents, and a domain model. PUNDIT has been developed cooperatively with the NYU PROTEUS system (Prototype Text Understanding System), These systems are funded by DARPA as part of the work in natural language understanding for the Strategic Computing Bat- tle Management Program. The PROTEUS/PUNDIT system will map Navy CASREP's (equipnlent casualty reports) into a database, which is accessed by an expert system to determine overall fleet readiness. PUNDIT has also been applied to the domain of computer maintenance reports, which is discussed here. Tile paper focuses on the interaction between the syntactic, semantic and pragmatic modules that is required for the task of making implicit information explicit. We have isolated two types of implicit entities: syntactic entities which are missing syntactic constituents, and semantic entities which are unfilled semantic roles. Some missillg entities are optional, and can be ignored. Syntax and semantics have to recognize the OBLIGATORY missing entities and then mark them so that reference resolution knows to find specific referents for those entities, thus making the implicit information explicit. Reference resolution uses two different methods for filling the different types of entities which are also used for general …

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