Báo cáo khoa học: "AUTOMATED DETERMINATION OF SUBLANGUAGE" doc

The parse file for the patient documents had correct parses for 236 sentences and sentence frag- ments; the file for the CASREPS had correct parses tor 123 sentences.. The grammar for th

A U T O M A T E D D E T E R M I N A T I O N O F S U B L A N G U A G E S Y N T A C T I C U S A G E

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 ~ I n t e l ~

Naval ~ Laboratory Wx,~hinm~, DC 20375

Lynel~ Hirxehnum

Research and Development Division System Development Corpmation / A Burroughs Company

Paofi, P A 19301

Abstract

Sublanguages _differ from each other, and from the "stan-

dard I a n ~ a g e , 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 ~ u r e 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

Introduction

y a oommumty ot s ~ t s m a t m ~ m g a resmctea

domain Sublanguages differ from each other, and tron}

the "standard language, in their syntactic, ~ a n t i c , 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 , y n ~ ' 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

p r ~ u r a l restrictions which capture v_.anous synt.t.t ~

and sublanguage _semantic cons_tt'aints "l]~e c o n ~ -

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 ~ entb'e me .- the number of times that each production m me context-free component of the grammar was applied in building the tr¢~ This yields a "trimmed" context-fr¢~ grammar for the sublangua!~e (consLsting ~ ~ o s c pro- ductions usea one or more tunes), atong w~m zrequency information on the various productions

This process was initially applied to text sampl~ from two Sublanguages The fi~s t is a set o.x s ~ pauent documents (including patient his.tm'y., eTam,n.ation, and plan of treatment) The second m a set ot electrical equipment failure relxals called "CASREPs', a class of operational report used by the U S Navy [Froscher 1983] The parse file for the patient documents had correct parses for 236 sentences (and sentence frag- ments); the file for the CASREPS had correct parses tor

123 sentences We have recently applied the process, to a third text sample, drawn from a subIanguage very stmflar

to the first: a set of five hospital discharge summaries , Which include patient histories, e~nmlnnt[ous, and sum- maries of the murse of treatment in the hospital This last sample included correct parses for 310 sentences

Results

The trimmed grarnrtl~l~ ~ d u ~ from thc three

sublanguage text samples were of comparable size The

grammar produced from the first set of patient docu-

menU; col~tained 129 non-termlnal symbols and 248 pro-

ductions; the grnmmar from the second set (the

"discharge summaries") Was Slightly ]~trger, with 134

non-termin~ds and 282 productions The grammar for the

CASREP sublanguage was slightly smaller, with 124

non-terminal~ and 220 productions (this is probably a

reflection of the smaller size of the C A S R text sam-

ple) These figures compare with 255 non-termlnal sym-

bols and 744 productions in the "medical records" gram-

mar used by the New York University Linguistic String

Pro~=t (the "medical records" grammar iS the Lingttistic

String Project English Grammar with extensions for sen-

tencc fragments and other, sublanguagc specific, con-

structs, and with a few options deleted)

Figures 1 and 2 show the cumulative growth in the

size of the I~"immed grammars for the three sublanguages

as a function of the number of sentences in the sample

In Ftgure 1 we plot the number of non-term/hal symbols

in the grammar as a function of sample size; in Figure 2,

the number of productions in the ~ as a function

of sample size Note that the curves for the two medical

sublanguages (curves A and B) have pretty much fiat-

tcned out toward the end, indicating that, by that point,

the trimmed grnmm~tr COVe'S a V~"y l a r ~ f r a ~ o n of the

sentences in the sublanguage (Some o f the jumps in the

growth curves for the medical grAmmarS refleet the ~vi-

sion of the patient documents into sections (history, pl3y-

sical exam, lab tests, etc.) with different syntactic charac-

teristics For the first few documents, wl3en a new see-

tion bedim, constructs are encountered which did not

appear m prior sections, thus producing a jump in the

c11rve.)

The sublanguage gramma~ arc substantially smaller

than the full English grammar, reflecting the more lim-

itcd range of modifiers and complements in these sub-

languages While the full grammar has 67 options for

sentence object, the sublanguage grammars have substan-

tially restricted mages: each of the three sublanguage

grammars has only 14 object options Further, the gram-

mars greatly overlap, so that the three grammars com-

bined contain only 20 different object options While

sentential complements of nouns are available in the full

grammar, there arc no i ~ t a n c ~ of such a:~[lstrllcfions in

either medical sublanguage, aad only one instance in the

CASREP sublanguage T h e range of modifiers iS also

much restricted i a the sublangu=age grammars as com-

pared to the full grammar 15 options for sentential

modifiers are available in the full grammar These are

restricted to 9 in the first medical sample, 11 in the

second, and 8 in the equipment failure sublangua~e

Similarly, the full English gr~mmnr has 21 options tor

right modifiers of nouns; the sublanguage gr~mma_~S had

fewer, 11 in the first medical sumple, I0 m" the second,

and 7 in the CASREP sublanguage Here the sub-

language grammars overlap almost completely: only 12

different right modifiers o f noun are represented in the

three grammars combined

Among the options occurring in all the sublanguage

grammars, their relative frequency varies ao~o~ding to

the domain of t h e text For example, the frequency of

prepositional phrases as right modifiers of nouns (meas; urea as instances per sentence or sentence fragment) was

0.36 and 0.46 for the two medical samples, as compared

to 0.77 for the CASREPs More striking was the fre- quency of noun phrases with nouns as modifiers of other nouns: 0.20 and 0.32 for the two medical ~mples, versus 0.80 for the CASREPs

We reparsed some of the sentences from the first set

of medical documents with the trimmed grammar and, as

~ , o.bserved a considerable " speed-up The t.mgumuc ~mng rarser uses a p.op-uown pa.~mg algo- rithm w i t h , ba~track~" g A,~Ldingly , for short, simple sentences which require little backtr~.king there was only

a small gain in processing speed (about 25%) For long, complex sentences, however, which require extensive backtracking, the speed-up (by roughly a factor of 3) was approximately proportional to the reduction in the number of productions In addition, the ~fyequcncy of bad parses decreased slightly (by <3%) with the l~mmed y m m r (because some of the bad parses involved syntactic constructs which did not appear m any o~,,~ect parse in the sublanguage sample)

Discussion

As natural lan ~,uage interfaces become more mature, their portability - the ability to move an inter- face to a new domain and sublenguage - is becoming increasingly important At 8 minimllm, portability requires us to isolate the domain dependent information

in a natural ]aDgua.~.e system [C~OSZ 1983, Gri~hman 1983] A more ambitious goal m to provide a discovery

procedure for this information - a procedure Wl~eh can

determine the domain dependent information from sam- ple texts in the sublanguage The tcchnklUeS described above provide a partial, semi-automatic discovery pro- cedure for the syntactic usages of a sublangua~.* By applying these t ~ g u e s to a small s u b l a n ~ sample,

we ~ adapt a broad-coverage grammar tO the syntax of

a particular sublanguage Sub~.quont text from this sub- language caa then be i~xessed more efficiently

We are currently extending this work in two direc- tions For sentences with two or more parses which

~ atisfy both the syntactic and the sublanguage selectional semanu.'c) constraints, we intena to try using t h e / r e - Cency information ga~ered for productions to select, a

invol "ving the more frequent syntactic constructs.** Second, we are using a s~milAr approach to develop a discovery procedure for sublanguage selectional patterns

We are collecting, from the same sublanguage samples, statistics on the frequency of co-occurrence of particular sublan guage (semantic) classes in subjeet.vedy.ob~:ct and host-adjunct relations, and are using this data as input to

* Partial, because it cannot identify new extensions

to the base gramme; semi-automatic, because the parses produced with the broad-coverage grammar

• must be manually reviewed

* Some small experiments of this type have been one with a Japanese ~ [Naga 0 1982] with 1|mired success B e c a t ~ of the v ~ _ differ~t na- ture of the grammar, however, it is not dear whether this lass any implications for our experi- ments

97

the grammar's sublanguage selectional restrictions

Acknowledgemeat

This material is based upon work supported by the Nalional Science Foundation under Grants No MCS-82-

02373 and MCS-82-02397

Referenem

[Frmcher 1983] Froscher, J.; Grishmau, R.; Bachenko, J.; Marsh, E "A linguistically motivated approach to automated analysis of military messages." To appear in

Proc 1983 Conf on Artificial Intelligence, Rochester, MI, April 1983

man, C "Isolating domain dependencies in natural

language interface P r o c Conf Applied Natural

Linguistics, 1983

[Greu 1963] Grosz, B "TEAM: a transportable

natural-language interface system," Proc Conf Applied

fional IAnguhflm, 1983

[Kittredge 1982] Kim-edge, 11 "Variation and homo- geneity of sublauguages3 In Sublanguage: Jmdies of

and J Lehrberger Berlin & New York: Walter de Gruyter; 1982

on and the concept of sublanguage In $ublan~a&e: sl~lies of language in restricted semantic domains, ed R

Kittredge and J Lehrberger Berlin & New York: Walter de Gruyter; 1982

[Marsh 1983] Marsh, E "Utilizing domain-specific

information for processing compact text." Proc Conf ied Namra[ Lansuage Processing, 99-103, Assn for

putational Linguistics, 1983

[Nape 1982] Nagao, M.; Nakamura, J "A parser which learns the application order of rewriting rules."

Proc COLING 82, 253-258

[Sager 1981] Sager, N Natural Lansuage lnform~on Pro-

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non-terminal symbols m the context-free com-

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Graph A: first set of patient documents

Graph B: second set of pat/cnt documcnts

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Graph C: e~, uipment failure messages

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as a fuaction of the size of thc text sample X

= the number of sentences (and sentence frag-

ments) in the text sample; Y = the number of

productions in the context-free component of

the grammar

Graph A: first set of patient documents

Graph B: second set of pati_e~.t documents

("discharge s.~,-,,~cs )

Graph C: e~, ,uipment failure messages

(cAs~,Ps-)

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