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<?xml version="1.0" standalone="yes"?> <Paper uid="C94-2122"> <Title>Automatic Recognition of Verbal Polysemy</Title> <Section position="4" start_page="762" end_page="763" type="metho"> <SectionTitle> 3 Polysemy in Context </SectionTitle> <Paragraph position="0"> '\]'he l)asic assumption of this w(irk is the stone as that Inade in pr(wious COl'pus-t)ased al)tn'oach(~s , i.(', SOlll.gtlltically simib/r words appeiu' ill ~t similar (xmtext. Senmnl.ically simihu&quot; verbs, for example, co-oc(:ur with the s~mm n(mns. The following sentences from the Wall Street Journal corpus show the t)oint: (s\]) New York Times said it offered to buy the shares of 1lop radio corl).</Paragraph> <Paragraph position="1"> (s2) tie may sell more shares in the Ollen market; or in 1)rive|to translu;tions.</Paragraph> <Paragraph position="2"> I1. is intuitively ol/vious that buy and sell are sema.ntitally feb|ted and that the semantic ('loseness (if these two verbs is ,nanifest(xl lly the fact that they ('o-oc('ur Wll,h 1,1l(~ SitlllP ll/lllll ,sh&\['(!s. &quot;igVo (!all l;hillk (If |ill ?b (timcnsional space, (~iL(:li dimension of wllich is associated wilh a speciiic noun aml in whi(:h ~ vm'b is assigned a. vector whose value of the i-th dimension is the wdue of mutual information (mu in short) \[Chur('h, 1991\] between the verb and the noun assigned to the i-th axis. If the 1)iu~i(: assumpti(m is correct, then semlmfic~dly similm' verbs form it cluster in 1:he Sl)ace, and t:herefore, sta.tistical clustering ~flgorithms can be ~q)iilied to verb vectors in order to discover semantic classes of verbs.</Paragraph> <Paragraph position="3"> Ih)w(,ver, this strltigh(;forw~trd method is often ha,lnpered by the existence, of 1)olysenmus words. The following s(mtences show potysemous usages of t~rke.</Paragraph> <Paragraph position="4"> (s3) In the past, however, coke has typically taken a minority stake in such ventures.</Paragraph> <Paragraph position="5"> (s3') Guber and peters tried to buy a stake in lllgill in 1988. &quot; (s4) That process of sort, ing out specifies is likely to take time.</Paragraph> <Paragraph position="6"> (s4') We spent a lot of time and money ill lmilding onr grou t) of sta.tions.</Paragraph> <Paragraph position="7"> (sS) Peol)le |ire queuing at the door to take Ills llroducl~ l/u |he dtlesn't have tile working capit.M to m~d~e the thing.</Paragraph> <Paragraph position="8"> (s5') Goodyear used i~twood trade credits lo olltltin, chemi(:;ds mid other products ;rod services in the U.S.</Paragraph> <Paragraph position="9"> We can nl~d(e the following obserwttions.</Paragraph> <Paragraph position="10"> 1. take and buy in (s3) ,md (s3'), take and spend ill (s4) and (s,I'), t~tke and obt,6n in (s5) and (s5') co-occm' with the noun sl.ake, time ~tnd product, respectively, mid the verbs of each of these pairs \]utve almost the stone SPllSO.</Paragraph> <Paragraph position="11"> 2. While certain usages of tttke have senses similm' to buy, spend, ~tnd obt~tin, these three specific v(~x'l)s h~tve distinct, senses and we hardly see synonymy itmong these verbs.</Paragraph> <Paragraph position="12"> In the space spanned by the three axes, each ass()ci~tted with stake, tim(', a.nd product, t.~tke does not constitute a clust.er with aaly of the three wu'bs, take co-occurs with the three iiO/lltS iLll( |hits high &quot;m,u v;-tll|es with t.heni, while \]lily, spend lind obtain have high m,u values only with one of the three nmms. Therefore, I.he (1.istaIK:c8 \[)el;WOelt take mid these three verbs are large &lid the synonymy of fake with them (lislq)petu's.</Paragraph> <Paragraph position="13"> \[n order to c~tpture the synonylny of ttflu, with the three verbs correctly, oHe has to deconipose the vector assiglled to take into three COlllpon()lit, v(~(Ttol'S, e~tch of which corresponds to the three distinct usages of take. The decomposition of a vector into i~ set of its cOral)Onent vectors requires i~ l)roller det:onqlosition of context in wlfich the wor(l occurs. Figure 1 shows tlw de(:onq)osition of the verb take in the thl'ee-dimensional spaces, takel, take2, iul(l take3 iLre the (:OmliOnent ve(:tors which ('olh~ctively ('onslitute the vector assigned to take.</Paragraph> <Paragraph position="14"> For the sltke of si,nplMty, we assume in |he ~d)ove t.hi~t tile three nouns chlu'~rcterise the contexl.s where the ver\]) la.k(~ o(:cttrs ;in(l, a,t 1.he slmm time, each of l.lwm ch;u'acterises n distinct usltge of take. IIowcver, ill iL ~j(?llcra\[ situ,%tion, ;~ \[l(llys(!IilO~lS V(~rll (:o-o(:(:ltrs with a bu'ge groull of nouns and one has 1;o divide the gl'Olt 1) of llOllliS inl;o it set of sullgroups, each of which correctly chm'acterises the context for a stlecific sense of the polysenmus word. The Mgorithm has to be able to determine when the cont.ext of & word should be divided and how.</Paragraph> <Paragraph position="15"> There m'e clustering algorithlns, called o,oe, rlappin, 9 cluste'rinf! \[Jardhw, 1991\], which allow ml entity t.(/ I)e null rithms assume that ewm an entity which belongs to more than one clusters is still a single entity. An entity behmgs to several clusters because it can be seen from several different viewpoints. 'rite same entity, for example, egg, can be seen as food, like bread, and as ingredients-of-food, like flour, at the same time.</Paragraph> <Paragraph position="16"> However, as we saw in the above, polyselnous verbs can be captured more naturally by seeing them as multiple entities, which hal)pen to take the same surface form. takel, take2 and take3 are distinct; entities (we (:all them hypothetical verbs in the following) with which different sets of nouns co-occur, and with which, therefore~ ditferent contexts are associated.</Paragraph> <Paragraph position="17"> Therefore, unlike standard overlapping clustering algorithms, our algorithm explicitly introduces new entities when an entity is judged polysemous and associates them with contexts which are subcontexts of the context of the original entity. Our algorithm has two basic operations, splittin9 and lumping. Splitting means to divide a polysemous verb into two hypothetical wwbs and lumping means to combine two hypotheticai verbs to make one verb out of them.</Paragraph> </Section> <Section position="5" start_page="763" end_page="764" type="metho"> <SectionTitle> 4 Measuring the Compactness </SectionTitle> <Paragraph position="0"> of a Group of Verbs The algorithm should decide when a verb has to he split into two hypothetical verbs. The decision is based on a measure of the sel-ilan~;ic compactness of a group of verbs. The semantic compactness of a group of verbs is a measure which shows the degree of dispersion of the group in an n-dimensional space. The compactness of a group of verbs, VG= {vl, v2, ..., v,~}, is defined as follows.</Paragraph> <Paragraph position="1"> 1. Let vi be one of the verbs v,, * .., v,,, and a vector assigned to vi be (vii, &quot; &quot;, vm). Each vij(1 < j <_ n) is computed by the following formula.</Paragraph> <Paragraph position="3"> IIere, mu(vi, n j) is the vahle of mutual informae tion defined in \[Chur Jr, 1991\] between t~i and nj.</Paragraph> <Paragraph position="4"> c~ is a threshold value given in advance.</Paragraph> <Paragraph position="5"> 2. The centre of gravity of a group of verbs, vl, * *., v,, is the mean vector of the vectors assigned to the verbs~ which is used to eompute the dispersions of the individual verbs in the group. The (:entre of gravity ~ = (gt,'&quot;, g~), and the length of it I 9 \[, are defined as follows.</Paragraph> <Paragraph position="7"> (2) 3. The dispersion, disp(vl,...,~4~), indicates the compaetness of a group and is defined ~ts:</Paragraph> <Paragraph position="9"> which have the same degree of dispersions. If I g I of A is larger than that of B, the absolute vMue of mu calculated for A is larger than that of \]3.</Paragraph> <Paragraph position="10"> This means that the absolute probabilities of co-occurrences of each notln and the verbs of A is larger than those of B; zus a result, A shouhl be judged to be semantically more compact than B.</Paragraph> <Paragraph position="11"> Therefore, the dispersion of (3) is amrmalised ms:</Paragraph> <Paragraph position="13"> disp,~o,, of (4) is prolmrdonal to the number of verbs. This means that a cluster of a greater number of verbs tends to be judged to be less compact than those, of a smaller number of verbs.</Paragraph> <Paragraph position="14"> Therefore, the dispersion of (4) should be fl~rther normalised to compensatc the effect of the number of verbs in a group. This normalisation is done by least square estimation. The result is (5), which will be used to measure the COml)aetness of a group of verbs.</Paragraph> <Paragraph position="16"> eient that is eml)irically determined by least square estimation 2.</Paragraph> <Paragraph position="17"> In the following, we use (5) as tlle wdue which shows the coml)actncss of a groul). A group with a smaller value of (5) is judged semantically more compact.</Paragraph> </Section> <Section position="6" start_page="764" end_page="765" type="metho"> <SectionTitle> 5 Clustering Method </SectionTitle> <Paragraph position="0"> ht lhis st,::lion, wc ltr:,s:,nl our clustering algorilhln.</Paragraph> <Paragraph position="1"> Wc first ('xplain the :)pert:ions of splittin+l Hlld hvmping.</Paragraph> <Paragraph position="2"> Th('n, we show th(, flow of the algorithm and Cxltlain how the whoh' algorit,hm worl:s.</Paragraph> <Section position="1" start_page="764" end_page="764" type="sub_section"> <SectionTitle> 5.1 Th(', Basic Idea </SectionTitle> <Paragraph position="0"> Tlw clust:ering algorithm prolmsed in this imlwr I)('longs to the ovorlapl>ing tyl):,. Tlw LC/I,. (1; ::: 1,?.,3,...) mvthod, prol)OSe(\[ I)y .Iardim', is (tilt, of th(, typical overbtppine; chtstt,t'ing algorithms \[Jardino, 1991\]. The os-.</Paragraph> <Paragraph position="1"> scntial dill'('rence l>etwoen ()Ill: algorithlu and tlw lit.</Paragraph> <Paragraph position="2"> ut('thod is thai out' algorilhnt txpli('itly introdtu,vs a (+ou(lit:ion when an cnt:it:y (+t verb) should It(, sl)li.I aim assigned t.o several clust, ers. In ottr method, wlu,tlwr }t VOI'\]) I! h}ls I;W() SOllS(!s 01' llOt i?~ judged I> 3' COml)aring tlw SOlll;-LltLic ('Olll\])a('l;1H'ss wthws of groups of V(,l'\])s {,0 It:' produced. Thai: is, (hero art' possil>ililios of creating tit(' following three clustvrs:</Paragraph> <Paragraph position="4"> +'i is a vvrb whose COOl'ditmte in an tl-(linwllnioltal Sl)a('o i> (v/i, &quot;&quot; &quot;, t'i,). +'ct aud v,J arc hypotho sisod verbs whose com'dinatcs in tit(' ii.:liltU,ttsional space are ma(\[o h'om tit(' (oordinah,s of It:(' original v(,rl) +'i by dividing Ill(, set o\[ nOUllS that ('ooccur wltlt I'i into two distinct sets. Tho division is math'in terms of two sets of nouns: ore'is the sol of nouns which co-c)<'('ur wit h ci,, and the ot her is tit(' set of nouns which co-occttr wit h QI'</Paragraph> <Paragraph position="6"> Not:' /hat il' lit(, noun associated wilh the dilm'n.sion j wld('h vo-o('('urs \vilh c i also ('o-o('(.urs with Itoth o1 cp and c,i, Ihc valu('s o\[ lit:' ,\] tit dinwnsimt o1&quot; ~'r~ and (,1. (V(L/ and vJi), art, tit(' same value, i.:'. the vaJm' ol' the '~ ~tr l>ol,ween thv ltOIltl ~tssol'iat (,tl wil It t lw jilt dinwnsio(t and el. \]'~url:llerltlol:e. if I lw noun associated wil It I he dimensiolt j, which ('o oc('l(rs with (,:, (loo,q llOt ('o-or':'(iv with \]):tilt v r and v,/, the vahu, of the 7tLtl, })t't\V('P(I {\]1:) ttOIt(( }IS'.</Paragraph> <Paragraph position="7"> sociated with the ./-tit (timcnsion and vl is set to 111(' values o\[ tit(' .j4h dinwnuion of eft. it:re, wv call this value lit:' surplus value. \Ve l'Oca\]l that lit(' COml>a('tn('ss value of a groult of t'i and +',t is snmlh,r than thai: of +,; aud f,p. This nwans thai the \[ornwr is more cotnl)a('l Ihan the laltcr. If Lhe surphts vahw is (tot sot to l)oth c(~ and c J, tit(, group of c.t aim +', t is more('Omlm('t than that of v(i ;LIi(I v v. '\['hcrefor(,, ill ordor lo lit}d((' UrI }/1l(l +'/3 as symmetrical as possibh', tit(' surplus vaha, is set 1o eft.</Paragraph> <Paragraph position="8"> \]:'un:'liou l, mp(l'(t, i,i~) has the opltosite ('tl'e:'t of tit(' \[uncliOll splil(v i, I'p, uq), i.e. it uwrges (!(~ and v,). Function lump(col, vfl) returns *~i.</Paragraph> <Paragraph position="10"/> </Section> <Section position="2" start_page="764" end_page="765" type="sub_section"> <SectionTitle> 5.3 Flow of the Algorithm </SectionTitle> <Paragraph position="0"> (:Hven a group of xerl)s, th, vu, &quot;&quot;, c,,. the algorithm prodm'es a svt of somantic clusters, which are ordered iu Iho a~,ceueting oMer of thvh' senmntic coral)at:hess values. 1\[' +'i is non-.l)Olysemoum it lwlongs to at least (tit(, o\[ tilt' l'Osltltatlt Sellla.llti(' ('htst(,rH. If it is l)olyse mous, the algorithm splits it inlo several hypolhetical verbs and each o\[' Ihom hC/longs lo at h'ast one of tlw soluatttic chlstcrs. The lhtw of lit:, algorithut is shuwn hi Figurc 2.</Paragraph> <Paragraph position="1"> As shown in Figure 2. tit(' algorithm is COml>osed of throe pro('odures: MakeqnitiabCluster-Set, Make-.</Paragraph> <Paragraph position="2"> Temporary-Cluster.-Set and Recognition-of-lOolysemy.</Paragraph> <Paragraph position="3"> 1. Make-Initial-Cluster Set Tlw procedure Make+Initial-Cluster-Set l)rmh:('es all possibh' pairs o\[ verbs in Ill:' input with thcir sclnantic ('oltt\]t}Wlll:,ss values. Tho resull: is a llst store the newly obtail}(,d ('htsl('r ; if the n(,wly ol}taine(: chtstt,r ('(}ntains all the v('r\])s in input then exit front the loop ;</Paragraph> <Paragraph position="5"> 2.</Paragraph> <Paragraph position="6"> 3.</Paragraph> <Paragraph position="7"> of pairs wl.i('h aro s(srt{'(l lit the ascon(ling or(l('r of their s(mlanti(' ('on}pa(:tnt,ss v;th}os. 'Fh(' list is called IC.S (Initial C.lusl(,r Set). 1CS contains ,,(,,- 1) pairs. In th(, :F()I/-lo()l I in lho algorithm, 2 a l)air (sf v('rlss is retri(,v{'d fronl ICS, (}n{' at ('a('h itt,ration, mM l)ass{,(1 to th(, next two pr(}('(,dur(,s.</Paragraph> </Section> <Section position="3" start_page="765" end_page="765" type="sub_section"> <SectionTitle> Make Temporary-Cluster-Set </SectionTitle> <Paragraph position="0"> The l)roc(,(hu'(, tM((,s two argulll('llts: 'fit(, first arglllll('llt is a pair (1t' verbs froul ICS an(l the s('('on(l on(' in a set (}f ('hlst('rs (C(!S - Crt'at('(l (?\]llSt('l.' Sot). CCS C()llsists (5:\[ the ('lltsl('l's whi('h ll~tV(' I)(,en ('r(!~tt(!(l st) far. \Vh('n th{' algorithn} t(~:'mi ll;Ltt's, CCS is th(, outllut of th{, algorithm. Make-Temporary-Cluster-Set :'t!lri(,vt,s tit(, (.htsl(,rs frolll CCS which ('ontain (me of th(' vcrl)s of th{, first argum(,nt (;t t)air f'r()m ICS). Th(~ ('htstt'rs thus l'Otri('ve(l fr(}ln CCS al'(' 15asse(l to tit(, nexI l)r()('('(llll'O l&quot;(/\]' further ('onsi(lt'ralion. If th(,r(, is n() CCS whi(.h ('()itt~tilts oil(' (1t' th(' v(u'lls of a pair fronl IC!S, a pair of v('rbs from ICS in stored in CCS as a n{'wly obtain{'d {'lusi (u'.</Paragraph> </Section> <Section position="4" start_page="765" end_page="765" type="sub_section"> <SectionTitle> Recognition-of- Polysemy </SectionTitle> <Paragraph position="0"> This procc(lure, which recogniscs a polysemous vt, r\]~, also tal,:(,s two ~trgult~('nts: th(' pair (}f v('rl/s from ICS and a set of chlst('rs :'('tri(,v('d l/y MakeTemporary-Cluster-Set. null W(' r('('all the dist'ussi(m lit s(,('li()lt 5.1. Let {t', ~t'l} I){' th(' pair of v(,rl)s frolll IC.S ~tlt(l { i,, ~t'2 } 1}0 (5:1(, (5i' the ('lust(,rs (5t' the se('(/n(I &rglllll(,llt, i.r. the ('lllS{:(!rs so f;u&quot; ol)lain(,d whicl, (:onta.in (me ()f the V('I*\])S, ~! ill the p~Lir. We have to (l('t(u'n}ilw wh('ther t11(' \,orb v has two s{,nses, which ('(irr{,sllon(ls t(i u,, and w2, resltcctiv(qy. This is {l('t(!rlni:wd 1)y ('Oml)a.ring the sont~tltli(' C(llnpa('tn('ss values ()f the thr('t' (liff{u'ent ('lust('rs shown in (6) and (7). Th{' ,splitting fun(tion (8) is a l)l)lied to I,, aq, and u,2 ~tn(1 1)rothw('(l newly hyl)oth('ti('al v(u'lls, *q and 1,2.</Paragraph> <Paragraph position="1"> Tilt' l.wm,ping function (9) is al)pliod to vt and u2 and lU~t\]:('s on(' verb ~, ()ut of th('m. If both of th(' S('lllallti(' ('(lllll)a('tll('SS vahl('S of ('a(:h sot sh(swl |ill (6) are smalh,r lhall :-i set shown ill (7), the srts (6) a.r(' s('h'('te(1, (}th(,rwis(,, (7) is scl(,('t(,(1 avd stored i,, CCS as a newly ol)taiu('d ('lnst(,r.</Paragraph> <Paragraph position="2"> If Bh(' newly ol)tain(,(l ('luster (lo(,s not contain all th(, verbs il} input, the n(,xt p~tir ()f v(,rl)s is l ak{!l} front lOS. ~tll(\[ th(&quot;ii th(' whole 1)ro('css is l:Ol){'al('(l.</Paragraph> </Section> </Section> <Section position="7" start_page="765" end_page="766" type="metho"> <SectionTitle> 6 Experiments </SectionTitle> <Paragraph position="0"> We ha.re ('ondu('t('(l two OXl)orinl(,nls. The first experiment is ('on('ern{'d with the ('lust(wing te('hniqu(, ~tn(l with verifying the eff(,t't ()f the l)r()l)t)s(,(l me/hod.</Paragraph> <Paragraph position="1"> The s('('oltd ('Xl/erilllOllt is ('Oll(hl('to(l to SOO h(}w vari(lU.S 1)+trt-{sf-slle('('h 1)&il's ;tfl'{'('{ the (qust(!ring resntts.</Paragraph> <Section position="1" start_page="765" end_page="765" type="sub_section"> <SectionTitle> 6.1 Data tbr the Experiments </SectionTitle> <Paragraph position="0"> 5\['h( ' ('orl)us we have us('(I is th{' Wall Str(C/et Jo'ur'~tal whi('h consists of 2,878,688 o('('urr(,nc(,s of part-ofspet'('h taggo(1 wor(ls \[Chur('h, 1991\], 73,225 (liffor(mt woi'(ls. \]}'l'Olll this ('orl)lls, \vo (sbtain('(l 5,9,10,193 wor(l pairs in ~t window siz(' of 5 words, 2,743,974 (lillk,r(mt w()rd pairs.</Paragraph> <Paragraph position="1"> 2{3 groups of v('rlss wet(' used in |lw ('Xl)orin~cnt:s, \]08 verb tokons with 56 dif\[i'rcl}t original forn\] of verbs.</Paragraph> <Paragraph position="2"> ~ti'li('s(, {gr(511lSS ('01ltailt i0 diff(u'rnt l)olyst,lnOUs \'orbs. Th(' groups of v('rlls are divid('(l into two diff{'r('nt tylst's, &quot;tyl)('\]' and &quot;@1}('2&quot;; %yl)c\] ' is a sel: of v(,r\])s ('Oilraining ()nr or mort, l/olys{m:ous v(,tbs, mt(I &quot;tyl)('2' (loos not ('ontain any l)o|ys(,mous verbs. Ea('h group is co:n1)os('(I of 3 to 10 ditf(,r(,nt v('rl/s. 'Fh(, seh,(:tion of v('rl)s of 't.yl)('l' was mad(' witll th(, illl('lltiolI of pro('{'ssing v(,rbs with wi(l(, usages, as i(h'}ltiti('(l in the Collins (li('ti(511ary and thesaUlUlS \[~XI('\[,('o(I, 199l\]. Tht'n, a llltlll-I}or of syn(snyms of the (:ht)s(m verbs w('rc st'h't't('d from th(' th('sam'us. Thr ('hlst(?l'int, ~ analysis is al)lllie(1 to {,a('ll grtsu l} :sf v(,rbs. Tim SktllS{' ('OPI/US and tile gro}tl)S of verbs ~tt'(' uso(1 throughout th(, (,Xl)orin:t,nts.</Paragraph> </Section> <Section position="2" start_page="765" end_page="766" type="sub_section"> <SectionTitle> 6.2 Experiment-I </SectionTitle> <Paragraph position="0"> Ill \]~\]xD(~l'illl(}llt~\[, w(' llS('(i voFb-ll()llll pairs, i.e. w(' as$51111{' all /t-(lilllOllSiOlt~tl Sl)a.('(,. ilt whi('h ~t verb is a,ssigned ~ reeler whos(' valu(' of the /-th (\[iln('nsiol} is Ill(' v,qhlo of mtt bi'lwe(!n tit(! vor}) a.lld the llOllll ~ts signed to the i-th axis. This ix l)ocauso, in tilt, small window sizes, Ill(, s(,}nantic relationshil)s between these two wet(Is mighl be quit(' strong, OSl)ecially those between ~t verb and its object whir'i: l/elunits the eff{,ctivo re('og,fition of vorlml lmlysomy. The inflected forms of tl,c sam(, llOltllS ~tll(l vorI)s art' troat(,d ~ts single units.</Paragraph> <Paragraph position="1"> For oxa.lnl)l(,, &quot;lilll(!'(lt()llll~ singular) an(l 'tiillOS'(noun, plural) are tl'o:%l od ,~ts sil}gh' milts. Wc obtained 228,665 diD'rent vor})-nolln pairs from 2,7,13,974 and Dr)ill tht's(', we seh,('ted 6,768 different vcrl)-liOllli pairs, 70:1 dit\[(!rcnt w'rl)s alld 1,79(5 llolitis Oil condili<)u lhat freqllell('i('s a,lld 7//,'//, SI, Y(&quot; llOl; hlw (,'V,,, > 5, .I Ill,r, .q) ~> 3) t</ pet'ntit ~L relial)le si:atis(i<'al analysis ;-/lilt tls('(l ~li('lll in lht, cxl>erhnent :l. Thc results are shown iu Tabh' 1.</Paragraph> <Paragraph position="2"> Tal)h, 1: '\].'he resuh.~ of \];\]Xl)erinl('ni-I</Paragraph> <Paragraph position="4"> hi Tal)h' ;1~ 'groul>' uieaiis the nundler </t' each group, ly\])c\] and t.yl)e2; ~('(11'l'('Cl;' lll('&llS thc llllllt\])('l' ()\[' <gl't/ltl)S of verl)s which are <'lustcrt,({ c<)rrc('tly: &quot;in('orrc('i&quot; means lhai. they are not.. Figure 3 shows t!acL s:-/lllill( ` of Ihe results, i.e. tyt)el-c<)rrect, tyl)o2-correct.</Paragraph> <Paragraph position="5"> tyl)el-incorrect, a.n(I type2-incorrect. \];\]a('h valu(' iu Figure 3 shows the vahte of 111(' .SClllStilli(' ('Ollil)a('l;tl('SS ()\[ ,h, g~l'Oll\]) ()\[ verbs.</Paragraph> <Paragraph position="6"> in lqgltre &quot;3, under the heading tyl)el-correct, we uan set, thai 'lake' is re('ogn\]sed ns a p(ll)'SCltlOliS v0rb siJl(\[ lias lhre(' (liff('rent S('ltS('.'-J, 's|)('ltd', &quot;btly', ali(I 'ol>i:ain'. \[11 &quot;/ similar way, &quot;close' has two diffcrl,ul SOllSOS, 'olld' all( |'opel1' S/lid s<,nianlically cl()sc v(ubs Stile grolll)('(\[ t.(){~(!th(,r. LTli(h'r Ih(' h('st(lilll{ type2correct s(,nlanti('ally similar v('rbs are groupc(l l(/gcther, ll<>wcver, un(ler l:he heading typel-incorrect 'lcavt&quot; is incorre('l:ly re<'og:iised as a n<)li-I)olysenious vcrl/; also under the heading tyl)e2-incorrect &quot;('onlc&quot; is in<'orrcctly re('ogl)ised as ;i l>olyscnl<>us verl/.</Paragraph> </Section> <Section position="3" start_page="766" end_page="766" type="sub_section"> <SectionTitle> 6.3 Experiment-II </SectionTitle> <Paragraph position="0"> Wt, have ('<>ndu('ted an exlwriuwtit; using t lw various \[n Tabh, 2, x-y shows the t3'lle of 1)arl>ol:sl)ec('h l)air of .c and y in this order, wher(' ,raml y art' qlw I)art of-sllet'('h ()\[' ill(' words. &quot;pair(l)' shows lht' numl)cr of difli'rcnl: 1)art of-sl/ee('h pairs frmn 2,713,974 and &quot;l)air(2)' shows th(' nuntber of different lmrbof-sl)ee('h l/ah's t)n ('ondition l:hat frequencies and 're, it re't. N,r~/ > 5. m ,(.c, y) > 3; .r and y show the ntunber (if different word. We used Lhesc ill E:,:l)erinwnt-II. The r('suh s are shown in Table 3.</Paragraph> <Paragraph position="1"> :) IIOle, N~:C/; is tilt' l/tllllh(!l' o\[' loia\[ ('o OC'{'lll'i'i~ll( &quot;t''~ o\[lht' WC>l'tls 3' illld ,I\] ill I, tliS order ill ii ~qndow.</Paragraph> <Paragraph position="3"/> </Section> </Section> <Section position="8" start_page="766" end_page="767" type="metho"> <SectionTitle> 7 Discussion </SectionTitle> <Paragraph position="0"> In l':Xl)erinwnt L de,',crihe(1 ill the 1)r('vious seciiou, 18 ()Ill of 26 groups of verlL~ art, aualysed <'ort'ecily antl the percentage attains 60.2 I/ in all. flow(we,', as shown i,t Table I, there arc 8 {>Troul)a which could nol i)C II('('OglliS('(l ('(I\]'I'('('i\]~L 'i'll(' t'i'\[Ol'S ;ll'(' classificd into t,wo iyl)e~,: t \[. Error.~ of recot, nilion of imlysclnoUS vcrl)s as nonqmlysemous ones; and '2. \]';rmrs of re('og-.</Paragraph> <Paragraph position="1"> uiliou of IIOII-\]:.OIVsI'IlH)IIS VOI'\])S lit-; 1)O13&quot;5Cl11OllS Oll('S, The IlllIlI1)(T Of gl'Olll/S classified iltlO each error type is \[ and 7, l'eSpeclivtqy. 'FIw causc o\[ Ihese crrol'S is lhaI, ('o-oct'l|rl'itlg ltOHllS shared by Iwo verbs sccm tt> 1/(, slanled ill these data. For exanq)h', (/l)s(q'vill(p, tilt' ('t)l'l)llS, W(' ('Sill St'(' thai &quot;h'avc' }l.~ls sl.I \]('~lHt \[\VO %('IIS('S. 'l'('tir(&quot; all(l 'l't'ltlaill'. The Following scnt('n('es arc I'roln the W.,ll b'tvt:ct ,h~lvcl~,al.</Paragraph> <Paragraph position="2"> (s6) I,:aplat: l('fl his jol_) al warncr-laml)erl.</Paragraph> <Paragraph position="3"> (s6') A1)oui 12 ':/ hay(' rqtireAl front a full-time .jol,.</Paragraph> <Paragraph position="4"> (sT) '\['hey can even h,avt, a sticky l)rob|('m, in the \[orni of higher brokerage conuuissions.</Paragraph> <Paragraph position="5"> (sT') but l'Cmain a. serious llro!flen A.</Paragraph> <Paragraph position="6"> l\[t)wevcr, tyl)el-incorrecf, ill l&quot;it~;ure 3 shmv<~ that 'leave is incorre('l},'~ r('cognised as a II(/iI-I)OI.V,qCiLI(/IIS verb. This error wa,~ caust,d I)y Ihc fa('t \[hat lhl' vahlc (/\[ tilt' S('ltKlllti(&quot; ('Olll|/S/('lltt'SS of &quot;r(,tirc' an(l &quot;l't'lilaill' was sntalh'r I ban t hal of any oI h('l' l>air of words illi(l 1/3 th(' |'acl thai Ill(' ('ardil:alily o\['a stq (IJ' li()lltlS whic\]: ('o-oc('ur with &quot;rt,lire&quot; alt(I 'l'('iilaill&quot; is larger thsl.ll IIH/I (1\[ Silly olht'r pair of words. \Ve 1)rovisionally ('on('htd(' f3tat, the use of verb-noun l)ail&quot;s a\]on(' is 110 |allpropriat(' for all the groups o\[ Vcl'l)s.</Paragraph> <Paragraph position="7"> hi Experintcnl-ll. th(, overall resulls are not as good a.s lhose of \];\]xperilnent l. }\]owever, wc could observe so111(, inte)'esting charat'le)'istics, uamely, some groups whi('h could lJol be anal3sed co)'recdy \]13 ' using verb noun pairs could lw analysed correctly 1 G&quot; using verb.</Paragraph> <Paragraph position="8"> adverb pairs or vt'rb-i)rvl)osiliol) pairs. The rcsulls show that 3 itll |o\[ 8 grt)ups such as tyi)el-incorrect iu Figure 3 whi('h were incorrect in E.':l)crinwnt-I coul(| 1)/' analysed corre('lly I)y using vcrll adverll pairs. Also, a.n t)the)' 3 groul)S su('h as type2-incorrect could bt' analysed ('orr(,('lly I)y using vcrl)-prel)osiiiou pairs. \~,c * I ~V(' lit) till{ ('ont;idor here getieral Ol'l'Ol'S o1' Selllgtllt, i(: clltsters, i.e. the cast, (d' Iwo verbs which are illll <,(,tmttgical/y clo<,c lint m'<' jml~4ed to ((lllSliltlJ(, ;t <-,t'lll}lllJit' el|isle\[', ll, ecause lifts kind o\['crl'()r did not occur ill Ihe cllt'te)ll oxpclillll'llls.</Paragraph> <Paragraph position="9"> can therefore exl)ect that w(, may l)e abh' to ol)tain more ac('ur;tte (:lusters by merging tiles(, thr(,e kinds of part-of-speech 1)airs into one larg(,r set. Because ~hese three difhwent 1)airs show distinct chara('t('ris~i('s of contexts in which a verb al)l)eacs. \'Ve have b(,(,n (ondu('dng more experiments (m these.</Paragraph> </Section> class="xml-element"></Paper>