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<Paper uid="C94-2105">
  <Title>Semantics WORD SENSE ACQUISITION FOR MULTILINGUAL TEXT INTERPRETATION *</Title>
  <Section position="3" start_page="665" end_page="667" type="metho">
    <SectionTitle>
2. TIPSTER TASKS
</SectionTitle>
    <Paragraph position="0"> TIPS'I~Et{ is a program of the U.S. government Adwinced I{eseareh Projects Agency (AI~,PA).** 'lb emphasize portability across languages and domains, the teams in 'I'IPSTEll. dat~ extraction were required to develop capabilities and perfbrm benchmark tests in two languages English and Japanese and two domains -microeh.'eCronics and joint wmturesresulting in four sets of bendunark results in each evaluation. The fi~lal evaluation, known as MUC-5 \[Sundhelm, 1993\], was held in August, 1993, and inehldetl the four TIPSq?I'~I{ data c'xtraction contractors as well as it;:; other sites from four countries.</Paragraph>
    <Paragraph position="1"> Figure 1 illustrates the basic TIPSTEI{ data extraction task. hi each configuration, systems process a sO, of texts and produce a set of database entries, or templates. The temple~tes are specified as pt~rt of each domain; thus the Japanese l, emplates in the joint venture domain are Mmost identicM in structure to the English joint venture templates. The task, for each tc~xt, combines the recognition of high-level concepts (such as the identitication o\[ a joint wmture in a text) **Our project, which included GE Corporate l/esearch and \])ewdopmen{,, the Center for Machine Translation at Carnegie Mellon University, and Mm:tin Marietta Managmnent and Data Systems (formerly tIE Aerospace), was one of four reruns in the data extraction component of TII~ST\]EH.</Paragraph>
    <Paragraph position="2"> with the discrimination of the meaning of iudividnal phrases (such as descriptions of products) and the resolution of references. D~r examl)le , Figure 2 shows a very simple example of a production joint venture between two companies.</Paragraph>
    <Paragraph position="3"> For each of these texts, the data that must be extracted includes the generation of typed objects (such as entities and relationships) and slot fills that incorporate information, either directly or through inferences, from l he texts. Much of this information comes from the recognition of highqevel entities and relationships such as that shown in Figure i. The rest includes much more detailed information, such ms the activity, fSeilities and financing involved in a joint venture. Figure 3 shows this part of the infornmtion for the samph; text, in tim format of the actual correct responses, with itMieized annotations to show where tile information cotnes from in the example.</Paragraph>
    <Paragraph position="4"> rHw slot; fills in T\[PSq'F,R templates include &amp;quot;set tills&amp;quot; drawn from a~ tixed list, such as the text code PRODUCTIOB for mauufaetm:ing and the nulnericM code 20 (&amp;quot;Food and ldndred products&amp;quot;)*** lbr processed tbod i)rodnction, &amp;quot;string fills&amp;quot; drawn from the ae***'\]'he tmmerical codes for the PRODUCT/SERVICE slot (and the groupings of the U. S. govermnent Standm'd hldustry Classification (SIC) scheme.</Paragraph>
    <Paragraph position="5">  KIKKOMAN COI'~ P. Wll ,I, 1 .INK UP WH'tl A TAIWANESE FOOD FIRM IN OCTOI~,I';R TO PI.~OllIJCF, SOY SAUCE IN TAIWAN, COMF'ANY OFI~ICI ALS SA \[I ) TI l URSIkAY.</Paragraph>
    <Paragraph position="6"> PRI,',SIDENT KIKKOMAN, CAPITALIZED AT 81) MIIA,ION TAIWAN YUAN (ABOUT 440 MILLION YEN), WILl, BE OWNED 50 PI,\]/(\],;NT EACI I I!.Y KIKKOlVlAN AND PRI.;SIDP;NT ENTERPP, ISES CORI'., TAIWAN'S LARGEST FOODSTUFI: MAKER, TIlE JOINT VI';NTURE WII,L MANUFACTURESOY SALlCE A\]'TIIE TAIWANESE FIRM'S H,ANTWITII KIKKOMAN'S TECIINOI+OGICAI, ASSISTANCF, AND I)ISTRII/UTI'; TIlE PROI)UCT UNDER THI:, KIKKOMAN IIRANI) NAME. 'Fill,', ANNUAL SAI,ES TAI.I.GF:I' lS S1'71' AT AROUNI) 3,0110 KII,OLITI~;RS W1TII\[N A I,'t,;W YEAI&lt;S, TI IEY SAIl). .... l,'igure 2: A s+unlflC iul)ut; l.cxl; &lt;FACII.H'Y 0659-1&gt; := I+oCKrION: Taiwan (COIINTP.Y) ,,.IN O(.'TOIII;'R 70 I'ROI)UCE SOY SAU('I,J IN TAIWAN, TYPE: FAC1OI~.Y &amp;quot;1711&amp;quot;, JOINT VI'~NTURF, WII,LMANUI,TiCTURI';SOYSAUCEATTHIi &amp;quot;IAIWANI':SI,', I&amp;quot;IRM'S &lt;INI)USTP, Y- (1659. I&gt; := I'IANT INI)USTI(Y- TYPE: PROI)UCTION I'I.~(}I)UCr/SERVICli: (20 &amp;quot;SOY ISAtJCtq&amp;quot;) &lt;INI)USTI.~Y- 0659 2&gt; := INI)USTIIY TYPE: SAI ,I,'.S _.ANIJI)ISTRIIHH'IiTHICI'RODUCI\].. PllOI)UC'I'/SEI/VICI{: (51 &amp;quot;SOY \[SAUCIt\]&amp;quot;) / (51 &amp;quot;\[TI IE I'ROI)LJCTI&amp;quot;) &lt;ACTIVITY 0659-1&gt; := 771E.IO/N/'VI~NI'URE WII,I,MANI/I,AC?'I\]RI,:SOYSAU(?EATTIll,; &amp;quot;IAIWANI;SI( I,'IRM'S tNIJUS'fI&lt;Y: &lt;INI )I/S'I'I&lt;Y 0659-1 &gt; t'I,ANT ACTIVH'Y SITI,;: (&lt;FAClI.ITYq)659 1&gt; &lt;};,NTFFY 0659 3&gt;) STAI.(TTIME: &lt;TIME 0659 l&gt; ...INOCTOBIqR'IOI'ROIJU('I&amp;quot;.SOYSAUUI,;IN &amp;quot;I;IlWAN, &lt;ACHVH'Y 0659 2&gt; := INI)USTRY: &lt;\[NI)LISTI&lt;Y. 11659 2&gt; ACTIVITY SITE: ('litiwan (('OUNTRY) &lt;I?,NTITY l)659 3&gt;) &lt;TIMI~ 11659 1&gt; := IIL/RING: 1(189 &lt;OWN\[';RSIIIP 0659 1&gt; ::: ...(,'AI'HTII,IZI{I)A'I'gOMII,LION &amp;quot;IAIWAN YUAN+.. WII,I, BE OWNI,,D 50 I'HC/(JI;NT EACII BY KIKKOMAN AND I'RESII)F+NT' I';N'I'I!RI'RISI,:S OWNED: &lt;I';NTFI'Y 0659 3&gt; CORP. TOTAL CAPI'FAI,IZATION; 80001)(100 TWI) OWNIiRSHII' .%: (&lt;ENTITY 0659 I&gt; 50) (&lt;I~N'HTY {)659 2&gt; 50) t&amp;quot;igtm~ 3: t}m't, oF (:orr('cL answer for t, ext; 0659  ta\]a,l Lcxt, xuch as ''SOY SAUCE'', pointers t,o ol;her ohj(,.cLs such as &lt;ER'TITY-0659-:I.&gt; aud a, wtrid;y or &amp;quot;tmrm~-dizc&lt;l&amp;quot; fills such as Taiwan (COUNTRY). Tim s&lt;.'t fills ol'Lcu Ca, l;t, ui:e. Io(;~d iul'orm;-tt, ion in Lhc l;cxl,, while the ol)j(~ct;s (consisl,ing 17t' a,n idctttiIi(n: wil;h ;-1 t'e lal;cd Letltpla,I;c fills) oFt, on involve infL't:~uces from many different Imrt;s of the Lext. For exmnplc, in Lhis case, the objcc, t, ACTIVITY+-0659-1 re\[h~cLs Lhc fairly subLlc disl, incl;ion lhaL th&lt;, vcnhu'e will be mamd'~{d,udng soy sauce aL l&gt;reside~nL l,',nl;(,rp|fises' phmt (the resull, oli rcI: crencc resolution) but that l, hc sMes will bo ca.tried ouL somewhere else in 'l'~fiwan (l, hc result, of a. real in I'ercnco+). \[n this part, of 1,he t;ask, tm+\ior object-tew!l decisions O\[I;CH hinge on the itll,crl&gt;l:(!l;;d,iotl o\[&amp;quot; t;he indi vi(hml wor(ts, ma, king |,he t,ask very l(~xicon--im;cnsiw:.</Paragraph>
    <Paragraph position="7"> |nL('rl&gt;rCl;ing the a.cl;iviLy itd'orma, Ci,:)n; LhaL is, McnLil'ying whaL each wult,ure is (loiug along wit, h l, he approl)ri+:d;c \[)lX)dtl(;Lx alld co(\[(?s, I:(;quil;CS I,;t,owb:xtgo. a.bout word us++ge in context. Activil,y words like build, e,s~aklish, and create axe .iusL as COIIIIIIO\[i as wot'(ls like pr'od't.:e and ma~t.uJhclure+ \[n rna, tty (:a,scs, whel;hcr xomc't;hing ix a ,joint vellt, ltrO a,&lt;'.t;ivil,y or not depends on ~ fairly detailed ;-ut~dysis of l, he.sc words &amp;quot;build.</Paragraph>
    <Paragraph position="8"> ing a \[a, clory&amp;quot; is dili'crcnl; fi'om &amp;quot;lmilding ;_~ nc;w pla, ne&amp;quot;, &amp;quot;l)uil(ling a, I)usin(.ss&amp;quot;, aml of c(mrsc, \['l:Ollt 'q)ldldiHg a prcseltO:?'. Th('.sc similar phr~l,X&lt;'.s &lt;m,u tic)l, Oflly O.vokc difl'ercnt; i&gt;roducL &lt;:(&gt;(los, lmL Mso cau ol'l;el~ M\[~!ct, lhl! high-level consLrua, l of a, sl,ory, 'l'hc ilfl,erl)rel,aLiol\]s o{' word scns,:~s cotJm 1,,agcl,her wit Jr &lt;lotmfiu ~md t+ask knc&gt;wl,+(Igc in exLra.ct, ing I;h,:~' a.pl+roprial;&lt;~ inl'ormai, ion fi'om t, Im++u I~hr~ses.</Paragraph>
    <Paragraph position="9"> Because ~me of 1,he go~ds of Lhis projocL wa,~; t.o dew;l&lt;, I) mot;hods o1' ha, ndling new dom~dns and lau guagc, s, il. w+~s import,a.*tk t,o cope. wil, h l;ilcsc &lt;q'ucb'd dill'o+rcnccs \]u word usa,ge i.u a geuct's.I way. This in&lt;tar lmrtilAouing tim knowledge o1 Lhc sysl;e\]n lilt,() f'O/il' coml)ouenLs: (1) gcm~ric, (:g)doma.in dul,eu&lt;hmL, (3) la.ll.gu:-tgedepctMot~L, aud (d) (\]o\[i'l:aiil a.il(l, latlgtlal,;c depcnd(mL. WiLh Lhc d(q;a.il ()17 a+llarlySis t, ha l imri,s oF Lll,~ task requir,c;, such ax I;hosc~ de+scribed al',,:)ve, it, i;; esscnl;iM not; only l,c, minimize l;hc ;-ttUO/llll; of ktlov, q ,:~(lgc that, is d&lt;'t)c'n&lt;hmt, on elf, her language or domain, bul; aim() to minimizv the off'oft, of acquiring knowledge tha, t, is dependent, on ciLhcr domain or bmgt)age, aud, eSl),:'.&lt;;ialiy , knowlodgc (ha.l, is d,~!lmndc\]ll, on bol;h. The sccl,ions thai, follow will covc.r t,hcse astmcl,s c~f Otll; so- null lntion to the TII'STEI{, problem.</Paragraph>
  </Section>
  <Section position="4" start_page="667" end_page="667" type="metho">
    <SectionTitle>
3. LEXICON &amp;: ONTOLOGY
</SectionTitle>
    <Paragraph position="0"> The previous section flamed some of the problems of data extraction in TIPSTEI{. with an emphasis on the aspects of the task that require substantial amounts of knowledge. We also presented our approach to the task by explaining tire synergistic objectives of creating generic resources and developing knowledge acquisition methods. This section will focus on the generic resources, while the next section will concentrate on acquisition methods.</Paragraph>
    <Paragraph position="1"> The main generic resource of SIIO(\] UN is its core ontology of about 1,000 concepts, which was developed to support GE's NLToolset lexicon \[,lacobs and Rau, 1993; Mcl{oy, 1992\] and had been tested fairly thoroughly on a variety of data extraction tusks prior to 'HPSTEI{. We augmented the core ontology using the CMU ontology from machine l;ranslation \[KBM, 1989\] and used the extended ontology as the basis tbr Japanese lexicon development. The idea of this effort was that the Japanese lexicon would mirror the existing English lexicon, allowing fbr sharing of tire domain independent components of the knowledge base across langnages as well as the sharing of any (lomain-specific knowledge that would be added.</Paragraph>
    <Paragraph position="2"> For example, the following is the English entry for the verb esiablish and its related forms:  The ,lapanese lexicon now consists of about \] 3,000 words. This is somewhat more than the. 10,000 unique roots of the English lexicon, but tire /,;nglish lexicon is still much richer in morphology and more thoroughly tested than the Japanese. Nevertheless, the two lexicons are roughly comparable and certainly eOmlmJ;ible. For example, the Japanese entry for sclsurilsu (~-~.) is the following:  The main link between the English and Japanese lexicons is through the :PAR field (for parent) in each word sense, which joins that sense to its parent in the ontology. In this case, the common parent betweeJt establish and selsurilsu, c-causal-event (the bringing about of events or effects), is a t'airly general category that includes two senses of ope~t as well as a variety of others like duplicalc iloll(\[ bridge. The reason that eslablish ends up in this general class is that it is very hard to confine any sense of the word to ereation events.</Paragraph>
    <Paragraph position="3"> I\]aving a shared ontology and lexicon format has certain adw~,ntages. It is a requirement for using a common language processing framework across langaages, and it ensm:es that words with similar meat&gt; ings in different languages end up with similar representations and ontological restrictions. The next section discusses how this coHufn)ll framework inllst be extended for domain-specific usage.</Paragraph>
  </Section>
  <Section position="5" start_page="667" end_page="668" type="metho">
    <SectionTitle>
4. ACQUISITION
</SectionTitle>
    <Paragraph position="0"> In a task like TIPSTEI/,, we cannot ca.ptm:e all the subtie distinctions that the task requires in the (:()re lexi-.</Paragraph>
    <Paragraph position="1"> con. Each domain, like joint ventures, requires a large amount of very specific knowh'.dge, not only about how words like eslablish behave, but also about simple racts like that oJ\]ice supplies usually includes things like pens and papers while office equipmenl usually inehnles machines like computers and copiers. Because many of these facts are at the intersection of world knowledge and word knowledge (that is, they are patterns of language use that relleet real-word concepts), even the most specific pieces of knowledge often seenl to apply across hmguages.</Paragraph>
    <Paragraph position="2"> The degree t;o which ontok)gy contributes to interpretation in any particular domain was, in geueral, somewhat less than we might have expected.</Paragraph>
    <Paragraph position="3"> For example, the category c-causal-event, inchnles not only words that don't haw~ anything to do with joint ventures, but also words thai in the .joint venture domain could be misinterpreted. The category in .... ~Jh ~&amp;quot; ~)L Japanese lnchldcs senses o words hke ~)i:~;~ an( {~:,~, which hehave, very similarly to sclsurilsu (iEgM.), but doesn't iuel ude many others tIntt a/so behave similarly.</Paragraph>
    <Paragraph position="4"> lit English joint ventures, the extended ('.lass of words used to describe the&amp;quot; establishment of a new con~l)a.uy includes plan, set 'up, form, and create. In ,lal)anese, the class i~n:ludes a~13&amp;quot;, /J~a~, ~f/a~, a~, &gt; &lt;, ~md  ,m~. \[n hoth (:a,s(!s, l;hese word classes ~u'e de.t,ermined from exaa'aining corpus da, l;{~, with a i)articulaa' empha sis on words Ih~l; a, re used to desct:il)e t;he tbrmat;ion of new companies. This includes words from different on-I,ologicaJ groups aud excludes cerbfin woMs from the c-causal-event e;ttegory.</Paragraph>
    <Paragraph position="5"> As wc ha,re l)oinl, ed out, words like c,~Rddisk +rod ,++clsuviL+u aa'e so eritica,l to the undersl;mlding of joinl, venl, ures that; ktlowle(lge td)oul; such words (:a.l bc \[mud coded \['O1' ea.(:h ta,lgua.gc' and (IomMn. Ilowcver, doin,~,; tiffs hand-coding for ina, ny aspects of the TIIWI'I'H{, task woutd not only involve au ext.raordinary amoutd; ol'eltbrt, IiIH; it; would thwarl; one (If I,lm maitl ohjcct,ives of the proje&lt;:t t,o develo I) methods that ease porl,a, hili~y ~teross langua,ges and domaius.</Paragraph>
    <Paragraph position="6"> Our &amp;quot;lMddlc groun(.l&amp;quot; s,.)lutiou to capt,H'iug t, hc more specialized k,lowledge, rulying heir, her or, gew.:ric knowledge nor on l~mgltttge spc&lt;:itic cm:odings, was to crc'a, te word classes to rcpres(ull, 1;tl(! informa t, io,l needed in the TII)S'I'EIi, dnta extra('tion task, Ix) a,pply these word cla.sses a crons hmgua.gcs, and t;o CXliand them using ~mtomated ('.orp/ls ;i, ila.lysis. ~V(! ol)sel;ve(\[ that, a, lt,hough ,lal)nnesc +rod English ha(I ditl'ereut vo cabularies atM properties, the ust~ge of words iu G~ch ,I;tl)attese corpus was very similar I,() the usage of (:ore iia, rable I:mglish words iu /:orpora f'l'Olll the s~unc do+ mnin. I:'()l' cxatnl)Ic, I, he word tq~tipm+'nl hl English joint ventures is w.ry simihu' to I, he woM ,~o'uch~ (: +'~.) in ,lat)~u.ese, and tiw l;ask Sl)ecillc dist, in(%ions are t;h(! same iu l:,nglish aml ,hq)am!se (e.g., the (listim:tious ~m,ong ,n,xlica\] cqUil&gt;,H++'n|, , l, ra, nsl),:)rl:atiot~ cqttil)meHt, , a,n(I elect;rieal eqtfil/nmu\[;).</Paragraph>
    <Paragraph position="7"> We U)ok a, dvant;~ge of I;his ohserwlLion itl &lt;hweloping a. two.sta.ge proe('.ss of (hwclopil~g word group h~gs across la.ugug~ges. Ou(:e the tmtjor groul/iugs were detined (w.a.nuaJly), l;hc autx)tna.l, cd I/IX)tess ()F COrl)US a,naJysis consisted of (l) eXl)a.,Miug word class(:s by associ+~t;ing con(Inon, t'el~d,ively unaml)iguot,s words with other classes, and (2) lurthcr CXlmmliu~.~ and hh',tify ing aJnbiguitics using a &amp;quot;llool,si;ral)l/ing&amp;quot; in'oc(~ss. 'l'h(~ I)oot, st,ral)l)ilu{; i)ro(:+!ss usc.:l the k,towh:dg,&amp;quot; that hnd already bee. cn(:o&lt;h'd I,o classi/'y a chunk of l,(!xL (ti&gt;r example, deci(liug tha, t a Im, rl,icuhu: l)hrasc described I, rallsl)orl, at, iOll eq.fil)nl(ml,), and assu,ni\[tg t,hal, wor(Is with a high degree of association with that (:al,cgory l'Gllnl; ;I.\[S() I)(! ,'et~d;e(I.</Paragraph>
    <Paragraph position="8"> The (it'at st,age o\[' Lhe pl'O(:css st,,~tl'LCd wit.h, E)r bo/,h E.glish ;u,d .lal)auese , a sel, of words !,hal, were closely i(l(;ntilied wit;h husiness ~ct, ivil, ies (lil,:c &amp;quot;tllaUUfa, cl;ures', a, nd &amp;quot;distrilmt, es'). Using a COrl)US ol7 shout, \[0 mi\]lion words (English I'rom l;he W:d! ,Vh'c+t ,/oar'n.al aud .l+q)a,mse from Nikk(:i ,5'hinblt;+), we t o(&gt;k the wo,'ds l, haJ. weI:C tnost, likely to a.l&gt;l)car within a window or three words of au &amp;quot;a.ct, ivi/.y&amp;quot; word, am:i iri(xt, tmmu Mly, to assigu them to pro, duct. classes. Tit(: ~+ta.t, isl.ical aualysis used a, weighl,ed mul,tiM in\['orm~d:iou statist, is.</Paragraph>
    <Paragraph position="9"> 'l'tds resulted in initiM ,~;roul)hlgs of words iul,o ch,~sscs corresponding to I)arti(:ular producl, groups, or codes.</Paragraph>
    <Paragraph position="10"> For example, the following ix IJ.: Euglish class sort( st)onding roughly U) SIt', cod(: 38, &amp;quot;Mcastlriug, analyzing, +rod controlling instrumsnl, s&amp;quot;: biomedical copier copiers lens lenses instrument pacemakers photocopy photocopier photocopiers radar navigational microfilm monitoring navigation guidance avionics photo photographic photography camera clocks watches eyeglasses suuglasses glasses Polaroid frames The second stage o\[ corpus ~malysis was the &amp;quot;hoot, st, rapping&amp;quot; process, I:rom t, he texts that included the &amp;quot;good&amp;quot; activity I,ernls, the program assigued ase(, of wor(\[ classes, such as thai, almvc, ha,sod on its exisLing l.:.llOWhxlge base. I&amp;quot;/)r exaanple, Jr&amp;quot; &amp;quot;eyeglasses&amp;quot; appeared in an activity LexL, t.hat (,ext would h,c nssigucd to group 38, a.long wiLh wh~tever other ca.t;egorh:s also ;H)l)earccl. 'l'hcn, I'or ca.oh word appearing ha every i;(}xI; ill tflle eOl:|)llS, w(! a.g;lill appIhxl the IIIIIIMIH.\[ in-I'orlna.tion sLaLisi,ie 1,o \[in,:\[ t, he siguilicant rcla.t;iouships hel:weeH wor(ls slit\[ gl'Otll)S. Whet, a wotx\[ could lie as-sociated with more t,han one grout) , this I)rocens iden tiffed phrases I,hat could help (,o distinguish Lhe woM sense, ;rod collecl;ed 811(:\[/ al\[ll)ig/l()tlS W(/I'(IS in t~ separa, I;e list, so that, they could be dealt wil, h ma, tma, lly, i\[' lie ccsn&amp; i:y.</Paragraph>
    <Paragraph position="11"> I:igurc 4, shows, for +~ .I ap;mesu sample, t;hc results of the c()l'pllS a cm, lysis I)rocess, itMudiug the identi\[iera, ion of the &amp;quot;producl:&amp;quot; words, with \['re(it.mci(!n a.ml weights, aml the anMysis of whether the corpus data (:oulirllie,:\] wtml, was knowll al&gt;ouL each word.</Paragraph>
    <Paragraph position="12"> In the TIPS'I'I':Ii+ hem:hmarl,:s, ,.re relied (m u.umally-corr(.ct, ed list,n, unillg I,he sl,a,t, inl,ic;d weighl;s only lo help i'es/)lw: dill'st'slices in select, big among Imll titdc' I)otent,ial product descriptious. Ilowcw'.r, in our own t,c.sl,s, we t'ouud the i)m'l~:)rmancc o\[' the ma, tmally (~dit;ed kuowhxlge (m the activity portion of the tern-+ pla/,e to bc only slightly helJ;er I, hau t, hc fully a ut, o mated sample. The kuowh',dge huse of word groups it.eluded over 4000 woMs in Iq,lglish uud over 2000 in ,\] &amp;pall(~Se.</Paragraph>
    <Paragraph position="13"> All,houglt SII()(~UN ban 1)Cell I,(!sLIM ilt a series ol' govcrnmcut hunchmarl(s, w('. still consider l his method to I)c ouly ~ sl,m'l,iug I)Oim,. There arc mauy i)rohlems, '1'11('. corpora used for l.:aiuing wer(~ not a good reprc+scut,a.t,ive sample, I)ecnuse t, hey were (h';twtl \['roHI dil\['ereul, sourc,::s frc+tu the t;est sa.mph.s due t.o limit.at.ions iu the a.vaila, bility o\[' rcl)rescntat;iv(~ l, rah~ing maPSerialn. The .Japanese training relied (m s(;glltenl;ing Lhe t;rainiJ G corpus inl,o words, a process t;\]l~l.t, occanio.mlly iutroduced error. ()thor sourc(~s of' error i t.:h.hxl eases where our initial manual groupiltgs iuvolv&lt;,d misinl;er prct;aLious of l;hc t;asl,:.</Paragraph>
    <Paragraph position="14"> Neverl,heless, hot:h the cor(~ ontology n.ud the au I,omal, cd l;ra.ildng reel,hod had ~ sigtdli,::aul, impac.I; ou SIIO(ltJN's renulls iu TIPS'I'I;;IL The Ilexl, seetiol.</Paragraph>
    <Paragraph position="15"> prcselH;s a. hrief SllllllIIF~ry O\[' /,h()s(! result,s.</Paragraph>
  </Section>
class="xml-element"></Paper>