On Representing Governed Prepositions and Handling “Incorrect” and Novel Prepositions

ON REPRESENTING GOVERNED PREPOSITIONS AND HANDLING "INCORRECT" AND NOVEL PREPOSITIONS Hatte R. Blejer, Sharon Flank, and Andrew Kchler SRA Corporation 2000 15th St. North Arlington, VA 22201, USA ABSTRACT NLP systems, in order to be robust, must handle novel and ill-formed input. One common type of error involves the use of non-standard prepositions to mark arguments. In this paper, we argue that such errors can be handled in a systematic fashion, and that a system designed to handle them offers other advantages. We offer a classification scheme for preposition usage errors. Further, we show how the knowledge representation employed in the SRA NLP system facilitates handling these data. 1.0 INTRODUCTION It is well known that NLP systems, in order to be robust, must handle ill- formed input. One common type of error involves the use of non-standard prepositions to mark arguments. In this paper, we argue that such errors can be handled in a systematic fashion, and that a system designed to handle them offers other advantages. The examples of non-standard prepositions we present in the paper are taken from colloquial language, both written and oral. The type of error these examples represent is quite frequent in colloquial written language. The frequency of such examples rises sharply in evolving sub-languages and in oral colloquial language. In developing an NLP system to be used by various U.S. government customers, we have been sensitized to the need to handle variation and innovation in preposition usage. Handling this type of variation or innovation is part of our overall capability to handle novel predicates, which arc frequent in sub- language. Novel predicates created for sub- languages arc less "stable" in how they mark arguments (ARGUMENT MAPPING) than general English "core" predicates which speakers learn as children. It can be expected that the eventual advent of successful speech understanding systems will further emphasize the need to handle this and other variation. The NLP system under development at SRA incorporates a Natural Language Knowledge Base (NLKB), a major part of which consists of objects representing SEMANTIC PREDICATE CLASSES. The system uses hierarchical knowledge sources; all general "class-level" characteristics of a semantic predicate class, including the number, type, and marking of their arguments, are put in the NLKB. This leads to increased efficiency in a number of system aspects, e.g., the lexicon is more compact and easier to modify since it only contains idiosyncratic information. This representation allows us to distinguish between Icxically and semantically determined ARGUIVIENT MAPPING and to formulate general class-level constraint relaxation mechanisms. I.I CLASSIFYING PREPOSITION USAGE Preposition usage in English in positions governed by predicating elements, whether adjectival, verbal, or nominal, may be classified as (I) lexically determined, (2) syntactically determined, or (3) semantically determined. Examples are: LEXICALLY DETERMINED: laugh at, afraid of SYNTACTICALLY DETERMINED: by in passive sentences SEMANTICALLY DETERMINED: move to~from Preposition usage in idiomatic phrases is also considered to be lexically determined, e.g., ~ respect to. 1.2 A TYPOLOGY OF ERRORS IN PREPOSITION USAGE We have classified our corpus of …

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