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<Paper uid="P85-1005">
  <Title>UNIVERSAI,ITY AND INI)IVll)UAi,ITY: TttI,~ INTI,~RACTION O1,' NOUN I'IIRASI,~ I)I,&amp;quot;I'I,~I,tMINi,~RS IN COI'UI,AR CI,AUSF, S</Title>
  <Section position="1" start_page="0" end_page="0" type="metho">
    <SectionTitle>
UNIVERSAI,ITY AND INI)IVll)UAi,ITY:
TttI,~ INTI,~RACTION O1,' NOUN I'IIRASI,~ I)I,&amp;quot;I'I,~I,tMINi,~RS IN COI'UI,AR CI,AUSF, S
</SectionTitle>
    <Paragraph position="0"/>
  </Section>
  <Section position="2" start_page="0" end_page="0" type="metho">
    <SectionTitle>
Arpanet: JCMA at MI'F-MC
Ahstract
</SectionTitle>
    <Paragraph position="0"> This paper presents ',,n iml~lcmcntcd thct~ry fnr quanlifying noun phr:.i.,.;cs in clausc.s ctmt:mting ct)pulnr verbs (e.g.. &amp;quot;he&amp;quot; ~md &amp;quot;bcCOIllC'). Ih(~ccctling fr()nl Icccnt thcorcticnl work I)y Jackcn(Ioll\[ I')X31. this c(,uputati(,tal theory recognizes the dependence ol&amp;quot; the quantification dccisit)n tm the dcl~nitcness, in(lclhfitcncss, or cla'~nncs.s hi&amp;quot; I)olh the subject and ()hjcct of c(apulnr verbs irt Fnglish. J~lckcndofl's intuition al')out Ihc qtmntil\]c:~tional interdependence of suhject :tnd ohjcct Itz~s hccn imported fn,n his broader cognitive thc()ry and rel'ormulated ~,tltin a ct)nstraint propagation iizmtcwt)rk, I-'xtcnsit,ts reported here includt: the additi,m of mort: :,ctive determiners, the expansion of determiner categories, and the tre~ttmcnt of dispktccd objects. A further linding is th::t qu:Httilicational constraints may prop~g:~tc across soine clausM boundaries. The algorithm is used by the</Paragraph>
  </Section>
  <Section position="3" start_page="0" end_page="0" type="metho">
    <SectionTitle>
I~.EI.AI'US Natural I.:mguage Understanding System
</SectionTitle>
    <Paragraph position="0"> (h,ring a phase of analysis that posts constraints to produce zt 'constraint tree.' This phase comes ~fter crcz~lit)n of synt~tctic deep structure zntd bcfure ~cntcntktl rcl'Clcltcc ill a semantic-network ntodcl. Incorp(~,'ation ()f the qtmntific~tion algorithm in a larger system that parses ~cntcnccs z,nd builds semantic models from thcnt makes RE/.A'FUS ~hle to acquire taxonomic and identity inlormatit)n from text.</Paragraph>
    <Paragraph position="1"> Introduction the qtmntilicz, tion of noun phrases.</Paragraph>
    <Paragraph position="2"> determining their tmiversulity or individuality, ix critical It~r the ~,utomatic acquisition of taxonomic and ~dcntity intbrm:ttion from natural language sentences. Automatic :~cquisition can convert ordinary texts into sources of tzL,~onomic and identity information lior use by learning and reasoning programs in artificial intelligence. Such information can also find use in efforts to develop setectiun restrictions from Icxical sources. Of course, proper qu~mtific:~tion of noun phrases also plays a key role in computer programs that cnde~wor to understand natural language.</Paragraph>
    <Paragraph position="3"> The theory for computing the qtmntilicational status ()1&amp;quot; noun phr:~scs fi)r tile czt.',e ,fl&amp;quot; copular verbs (e.g.. &amp;quot;he' and &amp;quot;become') wzus inspired hy recent tho)rcticzd w~rk ,fl' .I;~ckt'nthflT \[1~)X31 . .l~mkcn(ltdl' n,~lcd that qtl;llltiliC:.lliOn ,d&amp;quot; n~ itlll i/tlr:l~t:s li)r cl)ptll:lr ,,orbs dcpcn(.I.'.; jointly on IhC/ dole0 ~mnct~, ,fl' I-~tlt Ihc ~tfl~jut:t &amp;quot;NI ~ ,utd the ()hjcct NI' \[1')83, ')(1-~)1. %\].1 Ills intuiti, m h:.~s hr.:on rcli~rmulntcd. :~ttgtt~Cnlt:d, ~md implcntcr~tcd in the</Paragraph>
  </Section>
  <Section position="4" start_page="0" end_page="40" type="metho">
    <SectionTitle>
RFI.A'I'US N~lUlztl I.:ulgtt:tgc Undcr~t:u~tling S~tem. 2
</SectionTitle>
    <Paragraph position="0"> lllc implemented qu~mlilicztti,m thc,)ry is used by RFI.ATUS us it incrcmcntMly builds :t scmzmtic mC/)tlel. lhts method recovers clztn~ ~md identity inlbrmution from ~)rdinary English sentences. Alth()ugh the '~ctll~.mtic rll~dt:l tllUSt hE ,)CCZl~,i(mz,ll~ queried to re.'st)lve qtmntil\]cutitmzd :tmt)igttitic~. the tnt. tht~0 is primz~rily syntactic :rod does n~t rcqtfire rues, ruing. The c()mpttt~di(mM simplicity and hn&gt;zzd co~ct~lge oI&amp;quot; the thc,)ry zdh)w suct.'u.,,~,l'tH tltmntilqu:~tion ,d rl,~tlll phrz|st.:b ira most c()l)td;~r el;ruses. W,)tk b, in prt~grcs~ to c~tcnd tile an~d)'xis to p~rtitivcs zmd thcrchy yicltl zl c(~rnprehcn~,i~C/ analysis. While Ihi'~ ~lppr()at:h tl~cs not trc~,t ~u~.h difficult issues such z~s belief c()ntcxt'~ ~,,ttl mctz~l~ho,ic:fl u.'qlgcs, it tines :~tttlress mt,~.t lilct'nl cas,:x. Since the qu:mtific:ltion I. I will use &amp;quot;object NP&amp;quot; to refer to what ix I'ruquently called a  &amp;quot;'predicate objecL&amp;quot; 2. \[he experimental RELA'I US Naturai I.anguage Understanding System represents the con,bincd efforts of Gav:m I)t*ffy and the tmthor. Gavzm I)ttffy ix responsible for  the parser, the calegt)rial disaml')igu;itor \[\[)ully. 1985b\]. the lexicon and lexicon t, tilities. The author is respunsible lbr the representation system, the reference system, the component that nmps deep structure to semantics, the qtmntification system, the inversio, system, and the question-answering co,nponent.</Paragraph>
    <Paragraph position="1">  theory is deployed in a natural hmguage system that narscs sentences and bttilds a :~cmantic model from them, REIATUS bccotncs, among other things, a system Ibr acquiring class structure inlbrmation fi'om ordinary English texts.</Paragraph>
    <Paragraph position="2">  Sentence Men, lt'd into Scm'.lntic Representation T'he quantificati~m algorithm is embedded in a scntcntial const:aint-posting process \[l)ufl'y ;,,d Mallery, 1%41 shown in ligure 1. Scntential ctmstraint-posting ClC'\]tc~ a cott.slratt;t tree that ct)rrcsponds to n)ughly what t~m,d'~,m,~li, mal Clamm:aians call h~gica! Jbrm. !1~,e c,)~iStlmnt Irct' is tl.'St2tl to pcrl~.)lnt intcrscntcntial ~cfuruncc (merging succcs:,ive sentences into a single scmanlic-n,=twt~rk :n::dcl) \[Mallory, 19851. The input to c()nNtl'ailtt-i)osling phase is b~xh surf;.Icc struetttrc :.lnd dccl&amp;quot; StlUCltlrc cantlniczdizcd hy a It'an.,,Ibrmatit)nut pal:ser \[I)uffy, 19:Gal. In a dcplh-first, h~ttont tip walk of the dccp structure, omstrainus describing grammatical r,;latio.:, arc posted on otto-terminal paise-graph n(~d~. 3 When verbs in major clauses (Le.. clat;scs ()thcr than relative cl:m.',cs or clau:;al a(ljuncts) arc reached, they ,~upcrvise the quantilication of noun phrases they ctlmmand. If these verbs arc copular vcrhs, the copular interct)nsu'ainl algt)tith.,n is applied. In other cases, a,t)ther experimental algorithm pcrli)rms qttantification by drawing on logical rclatitms I'mm surface structure.</Paragraph>
    <Paragraph position="3"> The result of this process in the sentcntial constraint tree. It is a hierarchical description ~1&amp;quot; gramnmticz, I and logical rclati(ms that is suitable input lbr the reference s~stcm. By sequentially referencing the sentences o1&amp;quot; a text, a semantic mo(lcl of the text in incrementally constructed.</Paragraph>
    <Paragraph position="4"> The ('opular Inlerconstraint ,klgorithm Within a constrainl-p~sting framework, the basic task of NP qtmntilicatitm is to) decide whether to post a conslraint marking the NI.' as an individual or a universal. Y, incc the ta.nk ill','~d',C/N kn~wing the spccilic subject and ,)bjcct o1&amp;quot; a l~pular vcTh. it is delegated to a higher ctmstitucnt, the vcrh. 4 lhis dclcgatmn is motiwlted h), tile prin('ip/e ,lhwtzl ~h'('ision-,:m(ing which holds that dct:isions shtmld I)c located where all required inlilrnlali(m is htllh avadahlc and i~r~xHnalc. In Ilti~ case. only Ihc VCl'h kll()WX Ihc ~tlcntllicn ~1&amp;quot; hlflh Illfllll phrases duc it)the hic~archrC/al ',tructtt0'c ~dgrammat~cal Iclalions. &amp;quot;l'htm, whcll ;.I VClh iX~nlS its own iut'~lCllli;ll Ct)li511';lilllS, it also tlirccts the tttmntilicati(m o1 NPs that it d~,ninates (e.g., ils subject and ~lhject).</Paragraph>
    <Paragraph position="5"> This pmccthtrc ~,,~,s rcl~rntuiatcd m a C/~mstraint prtqmgat:t,n \[Waltz. 19751 I'ramcv.ta k ht:cut+sc I'caturcs t)f a single ctlnslilucnt cannt)l he tlcturmincd JntlcpcntlctHly o1 t~thcr tC~llSllltlCltlS ill the r,cnlcntial dcfivatitm. Since tlUantilicati~)ll:.ll c~ m~,traint pr~ ~pagatc.,, in t~ ~th di rccti&lt; ms. this i)llX.cNs is Zl t',pe ill C'~#t.~lillH'#ll &amp;quot;tllt'r~'t~/lSlrttittl. I-oltunalcl~, lhc p~ssihlC/ slates ~fl&amp;quot; &amp;quot;qlN arc ,rely lt)rctx tlclinilc, intlclinitc. :rod class. I~cuatmc IhC/ mlrnhcr of I':t~nsihlC/ NP statc~ is small (3) :rod the nuiHhcr of variahh:s is also ymall (2), a simple tahlc-lo~)kup alg,)r~thm &amp;quot;c(m)pdcs' s,bjcct and ohjcct qtmniil~caLions for al! p~)ssihle conliguration.s of NP dclinitcnc.ss. 5 3. At present, the REI.AIUS system builds scntcntial ccmstraints using the cammical grammatical relations ()1&amp;quot; the sentence, tile quantilicz~titm status of n~xm phr;LseS, and the truth valuc.'s of verbs. Work is in pn~grc.ss to incorporate |empnr:d constraints on verbs, temporal adjectives, and various types of context markers.</Paragraph>
    <Paragraph position="6"> 4. The RELATUS parser u~.'s non-standard parse graphs. A 'kern&amp;quot; corresponds to a clause while a &amp;quot;verbal&amp;quot; is something like a verb phrase except that the kern tells it what its subject. object, and m(~lificrs are at constraint-posting time. For further &amp;quot;details. see Du fl'y \[1985a1.</Paragraph>
    <Paragraph position="7">  llle actual task of determining the quantificational status of the NPs dccompt)scs into three steps.</Paragraph>
    <Paragraph position="8">  (1) The dcfiniteness of the noun phr,'kses is ,ascertained by examining the determiners and several othcr parameters. The algorithm is summarized by figure 2. Another algorithm dcscrihed by Iigure 3 is used Ibr determinerless plural NPs.</Paragraph>
    <Paragraph position="9"> (2) The quantificational slatus of the subject and object is (Ictermined by I(~)king each case tip in  the table depicted hy Iigt, re 4. Putentially ambiguous cases (marked with an ~tstcrisk) may require referencing the noun phrase in the semantic model to rcs~lve the ambiguity.</Paragraph>
    <Paragraph position="10"> Example scntences li)r Ihc ca.~:es in figure 4 are I'Oulld in Iigure 5.</Paragraph>
    <Paragraph position="11">  (3) The vcrb-phra.sc node informs each NP of its quantilicatit)n (the results of stcp 2), and they in 5. The conversion of constnunt propagation into a table  loq~kup approach is po~.sible in this special ~,,asc because there are only two variables, the suhjcct and the objccL In the general ~:a:~e, the sb, e of toe table is exponetitial in the number of ,/ariables.</Paragraph>
    <Paragraph position="12"> turn post corresponding constraints on themselves.</Paragraph>
    <Paragraph position="13">  In his discussion, ,hlckcndoll'\[lgX3: 77-106, esp.</Paragraph>
    <Paragraph position="14"> 8X-91.94-10hl nifty c~,tcguri/c.', determiners acc()r(ling to the distinction between definite and indefinite . I have added classm,ss It) his scheme m order tt) cope with such determiners as &amp;quot;all', &amp;quot;any' and &amp;quot;every'. While Jackendol'fs examples use only the determiners 'a'. &amp;quot;an', and 'the', I have Ibund intcrpretati~ms Ihr additional determiners which are summarized in figure 2. JackcndtflT considers proper nouns to be definite and the same is done here, except in certain cases t)l&amp;quot; phnal proper nouns which are interpreted a.s the plural indefinite (scc $21 in figure 5). The addition of the cla~s cate~urization calls lbr the'class determiners in the bottom of Iigure 2.</Paragraph>
    <Paragraph position="15"> The determiner. &amp;quot;no', is trevtcd as the negation of 'all.' Thus, the NP is quantified as a ckL~s and the copula negated. While SI0 and S18 in I\]gure 5 are valid, S19 is not. There are restrictions on where &amp;quot;no&amp;quot; can appear. It cannot modify b~)lh the subject ~md object.</Paragraph>
    <Paragraph position="16"> Nor can it modify the t~hjcct when the subject is indefinite (S19) ~r a universal ($2()). but it can when the suhj~:ct is tlcl\]nite (,%1~). lhc~c rcstricti~ms ~ccm generally valid Ibr literal cases cvcn lhough some idiomatic and mct,lphorical ctmsUttctions nlay vi(}late them.</Paragraph>
    <Paragraph position="17"> Vari()us casc:~ of dcturnlincr-lcss NPs are handled hy lhc alg,)rithm dial determines NP dcliniteness. Those t:ascs arc listed in figure 3. The indclhlllC category may hc incompletely handled hccause lhe thco,'y tlous not yet encompass partitives -- imlcfinite NPs dlat ix,rtitkm collcctiuns of individuals or universals. Thus, determiner-less NPs with plmal hc:;d nouns are not amdyzcd Ibr partitive readings.</Paragraph>
    <Paragraph position="18">  readings. Ihe Nl's they modify can be read as either collc~:tt~ms o1' individuals or universals. Some partitive determiners sttch tin 's()llle,' 'each,' 'nu~st', &amp;quot;few'. or &amp;quot;many' are tt.'.,cd It) make statements abemt subsets hi&amp;quot; a coil,cotton. With the exception of &amp;quot;some,' thc~,c are missing from&amp;quot; figure 2 pending research about how to determine their quantificatitm. 'Some' is interpreted just as an indefinite because o1&amp;quot; its high frequency. I hc detclmincrs 'all,' &amp;quot;any,&amp;quot; and &amp;quot;every,' wcrc included because they refer to the entirety of a collection. None of the partitive determiners, cvcn the ~mes currently used to determine dassncss, will be adeqtmtcly handled until completiun of continuing work ,m the syntactic parse graphs and the interactitm characteristics of partitives.</Paragraph>
    <Paragraph position="19"> S{~metimcs copular verbs take adjectives in the object position, leaving no apparent object. Some of these adjectives have a displaced ohjec/ as in $2, SI I, Sl3, .&amp;quot;;15 and ~16 in ligure 5. Wcrc there actually no-bject, the qtmntil\]cation or the subject w~tlhl bc determined in is~i'.'tiun (u.',ing a different algorithm). When the adjective has an ~bjcct. that object is u:;cd to perfimn the NP intcreonstraint with the subject. (Ja.,,cs C6 and C8 are imp~,,.,,ible ($22 and $24) unlcss the sentences have displact:d uhjccts (SI 1 and S13). tlowcvcr, this is not the cast: liar (26 where a copular verb is modal and has a partiuvc tletcrminer on its object. This suggests that pa~lilive readings of class determiners may make tht~e cases acceptable and that displaced objects simply make such a reading easier.</Paragraph>
    <Paragraph position="20"> Di.,pbwcd .~.hicC/'ts appear as the NPs to which &amp;quot;relative prememns'&amp;quot; brad in rula\[l',c clatlSCS or app~.',i'..ivcs. 515 prc~idcs an example of intcrc(mstraint acn~ss u relative clausc. There, &amp;quot;a phih,,~q~hcr' is the displaced ,~alhjcct &lt;~1' the diN~laccd ~hjcct, &amp;quot;:m hmian StOiC.' Interestingly. &amp;quot;a iqfih~:,~fdlcr ' i,~ al~,,~ a dihptaced object v, ith rc'q~cct tu &amp;quot;Mary.' Recall Ihaf. COl~,Stl'.~lint pusdnL: pr()ccc(ls Ih~m the bl)l.t()lll t)l' the ptllhC gr.:lph up lira hierarchy ~ffgrammattcal rcfaltt)ns with quanlllic:ltJon lidhw, ing al(mg and being g~vcrncd b~, major verbs. In SI5. qtlanttlicatl~m intcrctmS!.lamt is lirst ~;pplJcd to a philusolfller' and 'an hmian sit,it&amp;quot; by the cC/~ptnla t,l&amp;quot; the relative clause, then, rt is applied to 'Mary' and 'a phih~sopl~cr' hy the major copula. Since the fits! NP interc~'nstramt li,~cs &amp;quot;a phih~snphcr' as a universal, that rcsttll is then carri,.'d over int{~ the mtcrc~mstraint with 'Mary. In hoth S15 and St6. the qtmntilicati, mal constraint pr(~pa\[,atcs across ck, usal boundaries becm~se both clause share the ~me NP as an ohject and a subject.</Paragraph>
    <Paragraph position="21"> Ca.,,cs such as these should not lead to incemsistent quamil\]c:~titms. Instead. fl~e.~ slmuld aLzrcc. ,ttte:~tmg to the ~,,mndnc.,,s el&amp;quot; the algorithm.</Paragraph>
    <Paragraph position="22"> Jackcndc, IT \[I')83:971 a,gues that cases C4 and C5 in figure 4 are semantically ambiguous. This amhi~uity s~urns only to hold liar the determiner &amp;quot;the&amp;quot; and is i'es(,Ived b~- a simple rclcrence of the NP in the semantic representation. 6 If the ambiguous NP has no referent in the current discourse Ibcus \[Grosz, 1977\], then the NP must be a universal. If there is a referent, it is either a universal or an individual, and the same  (Dt, fi, Keness: L ~PS c) , (Quanlifiutlion: i ot c) indicates the ,ma!ysis of the NI&amp;quot; ,nder it. 7&amp;quot;he definiteness categories.&amp;quot; indefinite (i). definite (d). ttn,l class (c). ?'he quanlifiC/atLon categories.&amp;quot; mdivMual (i). class (c). &amp;quot; hldicales an ungrammalical senlenCe.</Paragraph>
    <Paragraph position="23">  quantification should be cht)sen. Where both appear in the discourse lbcus, the individual reading is preferred. This is partictdarly important lbr C4 because the status of the subject is needed to predict that of object. In either case, both rnust have the same quantificational status.</Paragraph>
    <Paragraph position="24"> The analysis of NP qtmntification in COl+ular clauses is signilicantly smtplilicd by the Ihct that there is no nccd to analyze qt,antilicr scoping. &amp;quot;l'his li.)lk&gt;ws fi'om the absence of ~, passive interpret:\[\[ion rt&gt;r copt, lar verbs. rhcy are specialized in ctmvcying classificational inlinmation rather than exprcxsing active changes of ~,tutc..&amp;quot;;into there is nu agent ,rod no t)hjcct which is acted uptm. passive c(m:;tructJ(ms can have no meaningful intc!prct:~tion. Interchanging the suhjcct and the object either has no cl'l~:cI t)n itlcntlly stattcmcnts t)l&amp;quot; inverts the  sonuntic m(Klcl o1' the sentences it analyzes, the CUl)ular intcrcnnstruint algorithnl allo,s a class structure it) be aut(mmtically ucqtnirctl. &amp;quot;Ihe way in which this inl0rmation is represented in R|-I.AI'US explt&gt;its the encoding scheme underlying English usage of Ct&gt;l)ular '.,'rbs. Ihis uncC/~(ling mctht&gt;d ulh)ws Ibur lypcs (;f linking ruluu(ms t() he cn'.:(~dcd iising a singtu It)ken. (i.e.. &amp;quot;be'). Ibis cnct~ding us stiunnlari/cd ili ligurc 6. Since the types can he dil'l~:rcntiatcd acc+~rding to the qtmntification of the nodes linked, the unique rcprc:;cnt;,titm of each link type docs not rcttuirc the introduction t)f ad hoc tokens. Orthnary and generic classilication arc used to construct the taxonomy. When two individtmls arc linked by a 'be' 6. Such a strategy h,'m bcen ff)llowed For other types of ambiguous preposition and clause bindings tHirst. 1981. 1984; Duffy, 1985bl.</Paragraph>
    <Paragraph position="25"> relattt)n, idcnthy between them is represented, rdcntity between two universals is represented with two generic classificatio,ts indicating that each universal is a subset of the uther. For predicate adjectives, a spccml token (e.g., 'HQ,&amp;quot; 'HAS-QUAlITY') is used as the relation and the adjective as the object in order to represent a tree-place property \[Winston 1980, t982\]. This avoids confusion when a ~,Vt)l'd token has rises Both as an adjective and a noun. Because REI.ATUS incorporates a theory of #Herprelive semantics, where syntactic cant)nicalization is perlbrmed on input and semantic equivalence is dcterminetl 4)nly tim)ugh reastming twcr a s)ntactically canonical rt:prescntation, this cnct;tling system is p:,rticularly appropriate, gecat,se no p(~st-proccs.,,ing is ncctlctt tt) &amp;quot;,uhstittHc tlistinct tokens fi~r the different types of linking rclalitms, this cnct)tling :lls() .umplil\]cs quanltl\]t.alitm t)r C()l'mlar el:roses, :.llld thcrel't)rc, the ctmsttamt p()sting p!&lt;)ccns in gcnc!ul. The cnct)ding rtlcth()(l t)011+,,' l~tltlilCS .3 '-,mall c(Hl'-,tctrtl, jnLIL':~:',c i\[I time li&gt;r walking the t.rcatctl clas.,, struc'tttrc, lhtls, the pq)tcntial gain in cllit.Jcr+cy hy ur, ing a mare uxplictt cnct)tling technique ts hint gin:if+ :mtl might hc ~)l'l~,ct by t)l hc! I,~ctors.</Paragraph>
    <Paragraph position="26"> Conclusions lhc ~opula, huurcon,~tra,nt algoriflun prc,xCflLcd ill tills paper flus hccn ~llll)lL,,,ill~L,, rtd3tlS, t m l;.ll~C L.d.\L applicatltms ovcr th,: pa:,t +vuai. ()nee the research on r~;+irt+tJvu.s iis cC/)nli'Jlctcd, the :+Ll+t)l :thm will ut)~ur :at+ even lalger i+l(+p(+rti(+n (~l C~+l+tll~tl- vulb ca~cs. Wt!llk 11:.15 hccn dollc 011 c()ptllnr qt~c~tit)ns hut Ls too c,,mplcx Ibr disctts,;Rm hurt:, largely due t(+ pragrnuttc inturacu++ns. Conjum.titms havc he,.:n treated ju.,,t like t~rdimtry NPs, ,gxo.'pt that crr,)r checking on,arc:+ that .ill ?;Ps ill conjutlctJt)vts agree in dcl\]nitcncss. Ihc idu:t urc()n~,traint prt~pagalitm has been ~:xtcndcd cxpcrhncnt:,lly to n~m-copular verbs using a dil'fcrcnt ptopugat:on alg&lt;+rtthln. llae approach has hcen succcssl'ul thus rtr.</Paragraph>
    <Paragraph position="27"> Ht)wcvcr, nlt)ae research is required to analyze intcr:!cti~ms between '.andros qu:mtificatitm alg~&gt;rithms and ':) .~l.',,cClt.~lJn lhc prt+f+ag'~tit,n characteristics or dil'furcnt verbs, accordint,, t(&gt; their senses and rncanings. Oti.'.mtlllcr sc~ping. :dgtJrlthm intu'ractkm, and diflicrential pr()p:tgatit)n are some t~l&amp;quot; characteristics ()r general ctmstitucnt interconstraint that make it more dil'licult. In gencf:tl, propagation ,ff q,,antificational constraints, seems a promisin~ approach =o previously rccalciu'ant problems. Even sO, strong psychological claims rot,st await further research and exhaustive atmlyses acros~ languages.</Paragraph>
    <Paragraph position="28">  Recent interest in developing lexicons to support computer understanding of natural language \[Walker and Amsler, 1985\] suggests die need for cfreetive methods of attgmcnting our Icxicographical knowledge using large corpora and unrestricted text. Selection restrictions are an important type of information to accumulate because they are needed not only to distinguish different senses of words but also to recognize metaphorical uses. Since accumuhttion of selection rcstrictio,s rctluires it, acquisition or taxonomic ialb.'mation is a priority. The coptflar intcrconstnlint algorithm introduced in this paper provides a basis lbr acquiring large taxonomics from unrestricted texts. A filter can be used to quickly pmnc all non-copt.lar sentences :t~ well as (lilfictflt copular sentences involving hclicl: and perhaps, time contexts. The remaining scntc))ccs can be parsed, quantified and represented in a large semantic model.</Paragraph>
    <Paragraph position="29"> This research would not only advance our knowledge of natur'd laxonomics and selection rc.~tricti~)ns h,t it woultl also generate empirical data tt:)clitl I~)r ttlose studying &amp;quot;dcfault logics' and stereotype hierarchies \[Minsky, 1975; Keil, 1979: Rotter, 1980; l-',rachrnan, 1982; Etherington and Rotter, 1983\]. One dil'l\]ct.ity with this research program is that an uncertainty principle is at work: The taxonomy used to determine selection restrictions itself depends on recognition of nlctaphors fllrottgh selection restrictions. Success in this lexicographical task will require the careful development of effective research strategies.</Paragraph>
  </Section>
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