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<Paper uid="C00-2128">
  <Title>A Statistical Approach to the Processing of Metonymy</Title>
  <Section position="6" start_page="887" end_page="889" type="evalu">
    <SectionTitle>
5 Experiment
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="887" end_page="888" type="sub_section">
      <SectionTitle>
5.1 Material
Metonymies Seventy-five lnetonymies were
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      <Paragraph position="0"> used in an ext)erilnent to test tile prol)osed lnethod. Sixty-two of them were collected from literature oll cognitive linguistics (Yamanashi, 1988; Yamam~shi, 1995) and psycholinguistics (Kusumi, 1995) in Japanese, paying attention so that the types of metonymy were sufficiently diverse. The remaining 13 metonymies were direct translations of the English metonymies listed in (Kalnei and Wakao, 1992). These 13 metonylnies are shown in Table 2, along with the results of the experiment.</Paragraph>
      <Paragraph position="1"> Corpus A corpus which consists of seven years of issues of the Mainichi Newspaper (Dora 1991 to 1997) was used in the experiment. The sentences in tlle cortms were mort)hologically analyzed by ChaSen version 2.0b6 (Matsumoto et al., 1999). The corpus consists of about 153 million words.</Paragraph>
      <Paragraph position="2"> Semantic Class A Japanese thesaurus, Bunrui Goi-tty6 (The N~tional Language Research Institute, 1996), was used in the experiment. It has a six-layered hierarchy of abstractions and contains more than 55,000 nouns. A class was defined as a set of nouns which are classified in the same abstractions in the top three layers.</Paragraph>
      <Paragraph position="3"> The total nmnber of classes thus obtained was 43. If a noun was not listed in the thesaurus, it was regarded as being in a class of its own.</Paragraph>
    </Section>
    <Section position="2" start_page="888" end_page="888" type="sub_section">
      <SectionTitle>
5.2 Method
</SectionTitle>
      <Paragraph position="0"> '.1.11(; method we have dcseril)e,d was applied I;O the metonynfie, s (lescril)e,(t ill section 5.1. Tile 1)r()eedure described 1)clew was followed in intert)rel;ing a metonynly.</Paragraph>
      <Paragraph position="1"> 1. Given a mel,onymy of the, form :Noun A Case-Marker R Predicate, V', nouns re\]al;e(l to A 1)y A 'n,o .1:1 relation an(l/or A near H relation were extra(:ix'~(l from 1;he, corl)us described in Se(:tion 5.\].</Paragraph>
      <Paragraph position="2"> 2. The exl;racted llOllllS @an(lidatcs) were  ranked acc()rding t() the nw, asure M d(;tined in \]{quation (3).</Paragraph>
    </Section>
    <Section position="3" start_page="888" end_page="889" type="sub_section">
      <SectionTitle>
5.3 Results
</SectionTitle>
      <Paragraph position="0"> The r(;sult of at)l)lying the proi)osexl me, thod to  our sol; of metol~ymies is summarized in 'l'alfle 1. A reasonably good result (:an 1)e s(;cn for q)oi;h r(,\]ai;ions', i.e. l;he result ot)i;aincd \])y using both A no 11 an(t d ncm&amp;quot; 1\] l'elal;ion~; wllen  extracting nouus fl'onl th(' cOllmS, \[1'1~(', a(:(:ura(:y of q)ol;h re, l~tions', the ratio ()f lhe nllnil)er of (:orrc(:l;ly intcrl)r(;te,(1 (; t()l)-rank(;(l (:an(li(lates to l;he, total mmfl)er of m(',l;()nymies in ()it\]' set, w,,s 0.7:, (=5',Visa+22)) alld ('ol,ti(t(' l,ce inWwva.1 estimal;e was t)(;l;ween ().6\] an(t 0.8\].. \Y=e regard this result as quite t)ronfising.</Paragraph>
      <Paragraph position="1"> Since the mc, i;onymies we used wcr(; g(m(u'a\]: (lomain-in(lel)(',ndca~t, on(s, l;h(~ (legr(', ~, ()f a(:curacy achi(;ve, l in this (~xp(;rim(;nt i~; likely t() t)(; r(',t)(',al;e(l when our mePShod is ~q)l)lie(l t() oth(;r genural sets ()f mel;onymies.</Paragraph>
      <Paragraph position="2">  Tal)le 1 also shows that 'both relations' is more ae(:ural;e than (',il;her the result obtained 1)y solely using the A no \]3 relation or the A near B relation. The use of multit)le relations in mel, onyn~y int(;rl)retation is I;hus seen to l)e 1)enefieial.</Paragraph>
      <Paragraph position="3"> aThe correct;hess was judged by the authors. A candidat(; was judged correct when it; made sense in .Ial)anese. For examl)le, we rcgard(;d bet:r, cola, all(l mizu (W;d;el') as all (:orr(!c\[; intcrl)r(~l;ations R)r glas.s we nom, u (drink) (drink a glass) because lhey llla(le ,q(~llSC in some (:ontcxt. Table 2 shows the, results of applying the method to the, thirteen directly translated metonymies dcscril)ed in sect;ion 5.1.. Asterisks (*) in the tirst (;ohlillll indicate that direct translation of the sentences result in unaccel)table Japanes(;. The, C's and W's in t;he second eohmm respectively indicate that the top-ranked ('andi(latcs were correct and wrong. The s(;nten(:es in the l;hir(t column are the original English metonymi(;s adol)tc, d fl'om (Kamci and \Y=akao, t992). The Japanese llletollylllies in th(: form hloun ease-lnarker predi(:ate 7', in the fourth column, are the illputs I;o the method.</Paragraph>
      <Paragraph position="4"> In this ('ohunn, we and 9 a mainly r(;present I;he ac(:usal;ive-casc and nominative-ease, re-Sl)ectively. The nouns listed in the last eolmnn m'e the tot) three candidates, in order, according to the. measure M that was defined ill Equation (3).</Paragraph>
      <Paragraph position="5"> Th(,,se, l'csull;s (lemonstrate the et\[~(:tiveness of lhe m(',thod. '.l&gt;n out of t;11(: 13 m(;tonynfies w(u'c intc, rt)rete,(l (:orre, ctly. Moreover, if we rcsl;ri(:t our al;l;(',nti()n to the ten nietonylHics i}mt m'e a(:(:Cl)tal)le, ill ,/al)anese, all l)ut one w(;rc, inl;('rl)r(;te(t (:orrectly. The a(:curacy was 0.9 ---- (/)/\]0), higher than that for q)oth relations' in Tal)le i. The reason fi)r the higher degl'ee of ac(:tlra(;y is l;\]lal; the lll(;|;Ollyllli(;s in Tal)le 2 arc semi,what tyi)ical and relativ(;ly easy to int(~rl)rel; , while, the lnel;(nlynlics (:olle(:l;c(t fl'()m ,lal)anese sour(:es included a (liversity of l;yl)es and wcr(~ more difficult to intext)let.</Paragraph>
      <Paragraph position="6"> Finally, 1;11(', efl'ecl;iv(umss of using scnlanl;i(: classes is discussed. The, l;op candidates ot! six out of the 75 metonynfies were assigned their al)prot)riatenc, ss by using their semantic classes, i.e. the wducs of 1;11o measure 114 was calculated with f(H,/~, V) = 0 in lgquat;ion (6). Of the, se, l;hrce were corrccl,. 011 l;hc, other hand, if scmanl;ic class is not use(l, then three of the six are still COITeC|;. Here there was no lint)rovemerit. However, when we surveyed the results of the whole experiment, wc found that nouns for wlfich .fiB, R,, V) -- 0 often lind (:lose relationship with exl)licit terms ill m(;tonynfics and were al)propriate as interpretations of the metonynfics. We need more research betbre we (:an ju(lgc the etl'ectivc, ness of utilizing semantic classes.</Paragraph>
      <Paragraph position="7"> rPl'edicatcs are lemmatized.</Paragraph>
      <Paragraph position="8">  Ite read Mao.</Paragraph>
      <Paragraph position="9"> We need a couple of strong bodies tbr our team.</Paragraph>
      <Paragraph position="10"> There a r___q a lot of good heads in the university.</Paragraph>
      <Paragraph position="11"> Exxon has raised its price again.</Paragraph>
      <Paragraph position="12"> glass we nomu yakan ga waku Ford we kau Picasso we motu Stcinbcck we yomu Bach we hiku Mao we yomu karada ga hituy5 atama ga iru Exxon 9 a agcru Washington is insensitive to the needs of the people.</Paragraph>
      <Paragraph position="13"> Washington ga musinkci C The T.V. said it was very crowded at; the festival.</Paragraph>
      <Paragraph position="14"> W The sign said fishing was prohibited here.</Paragraph>
      <Paragraph position="15"> T. V. 9a in hy&amp;siki ga iu beer, cola, mizu (water)  this pnper identifies implicit terms fbr tile explicit term in a metonymy. However, it is not concerned with the semantic relation between an explicit; term and implicit term, because such semantic relations are not directly expressed ill corpora, i.e. noun phrases of the form A no B can be found in corpora bul; their senmntic relations are not. If we need such semantic relations, we must semantically analyze the noun phrases (Kurohashi and Sakai, 1999).</Paragraph>
      <Paragraph position="16"> Applicability to other languages Japanese noun phrases of the form A no B are specitie to Japanese. The proposed method, however, could easily be extended to other languages. For exmnple, in English, noun phrases B of d could be used to extract semantically related nouns. Nouns related by is-a relations or part-of relations could also be extracted from corpora (Hearst, 1992; Berland and Charniak, 1999). If such semantically related nouns are extracted, then they can be ranked according to the measure M defined in Equation (3).</Paragraph>
      <Paragraph position="17"> Lexically based approaches Generative Lexicon theory (Pustejovsky, 1995) proposed the qualia structure which encodes semantic relations among words explicitly. It is useflfl to infer an implicit term of the explicit term in a metonymy. The proposed approach, on the other hand, uses corpora to infer implicit terms and thus sidesteps the construction of qualia structure. 8</Paragraph>
    </Section>
  </Section>
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