Automated Determination of Sublanguage Syntactic Usage

AUTOMATED DETERMINATION OF SUBLANGUAGE SYNTACTIC USAGE Ralph Grbhman and Ngo Thanh Nhan Courant Institute of Mathematical Sciences New York University New York, NY 10012 Elalne Marsh Navy Center for Applied 1~se, arch in ~ Intel~ Naval ~ Laboratory Wx,~hinm~, DC 20375 Lynel~ Hirxehnum Research and Development Division System Development Corpmation / A Burroughs Company Paofi, PA 19301 Abstract Sublanguages _differ from each other, and from the "stan- dard Ian~age, in their syntactic, semantic, and discourse vrolx:rties. Understanding these differences is important'if -we are to improve our ability to process these sublanguages. We have developed a sen~.'- automatic ~ure for identifying sublangnage syntact/c usage from a sample of text in the sublanguage..We describe the results of applying this procedure to taree text samples: two sets of medical documents and a set of equipment failure me~ages. b Introduction A sub~age.is th.e f.oan.of ..natron." ~a~ y a oommumty ot s~ts m atm~mg a resmctea domain. Sublanguages differ from each other, and tron}. the "standard language, in their syntactic, ~antic, anti discourse properties. We describe ~ some rec~.t work on (-senii-)automatically determining the.syntactic_ properties of several sublangnages. This work m part ot a larger effort aimed at improving the techniques for parsing sublanguages. If we esamine a variety of scientific and technical sublanguages, we will encounter most of the constructs of the standard language, plus a number of syntactic exten- sions. For example, report" sublantgnag ~, such as are used in medical s||mmarles and eqmpment failure sum- maries, include both full sentences and a number of ~ag- merit forms [Marsh 1983]. Specific sublanguages differ in their usage of these syntactic constructs [Kittredge 1982, Lehrberger 1982]. Identifying these differences is important in under- standing how sublanguages differ from the Language as a whole. It also has immediate practical benefits, since it allows us to trim our grammar tO fit the specific sub- language we are processing. This can significantly speed up the analysis process and bl~.k some spurious parses which wouldbe obtained with a grammar of Overly broad coverage. Determining Syntaai¢ Usage Unf .ort~natcly, a~l..uirin~ the data .about ,yn~'c usage can De very te~ous, masmuca ~ st reqmres .me analysis of hundreds (or even thousands) of s~. fence., for each new sublangnage to.be proces____~i. We nave mere- fore chosen to automate this process. We are fortunate to have available to us a very broad coverage English grammar, the Linguistic.St~ing Grammar [S~gor 1981], which hp been ex~. d~ include the sentence fragn~n_ ts of certain medical aria cquilnnent failure rcixn'm [Marsh 1983]. The gram,--," consmts of a context-~r=, component a.ugmehtc~l .by pr~ural restrictions which capture v_.anous synt.t.t ~ and sublanguage _semantic cons_tt'aints. "l]~e con~- . component is stated in terms ot lgra.mmatical camgones such as noun, tensed verb, and ad~:tive. To be. gin .the analysis proceSS, a sample .mrpus is usmg this gr~,-=-,: .The me of generanm par~s_ m reviewed manually to eliminate incorrect ~ . x ne remalningparses are then fed to a program which .cc~ts -- for each parse tree and .cumulatively for ~ …

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