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<Paper uid="C94-1006">
  <Title>Two Methods for Learning ALT-J/E \]Y=anslation Rules from Examples and a Semantic Hierarchy llussein Ahnuallim ln\[o. and Coml)uter Science Dept. King Fahd University of l)etroleum and Minerals l)hahran 312(;1, Sated( lXrahia</Title>
  <Section position="7" start_page="61" end_page="62" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper reported our work towaMs the acquisiti(m of,hqmnese-lCmglish translation rules through the use of inductive machine learning techniques. Two approaches were investigated. The first aplmmch ix based on a. theoreticMly-f(mnded algorithm given by l lmlssler fl}r h~arning internal disjunctive eoncel)tS.</Paragraph>
    <Paragraph position="1"> This algorithm haLs the advantage that it is tailored to utilize background knowledge, of the kind availabh~ in our domain. We f{nmd, howeww, no obvious way to make this algorithm learn directly t'mm ambignous training examples, and thus, anlbiguity wlm explic-.</Paragraph>
    <Paragraph position="2"> itly removed from the training exmnph~s in order to use this algorithm. Om' second apl)roach ix based on the IDa algorithm. As it is, i1)3 is not Mile to utilize the background knowledge of our domain, nor is it capable of dealing with ambiguous training exam-I b'Xallll)h, s ill't' vxchldell frOlll the tl'aillillg st,t Ollt * ~l\[ il IilllO. :\[ho i'llI(, hqllllt'd \[iOlll I\]lo l'('sl of ~hl, I'Xalltllllt's is thlqt IINl'd to l}rPdict the {'lass o\[ tilt, l'lqllOXl,d eX;tllllllt.. This ',',';Is I'{'I}{'atod for all lhe (,Xilllll}lus. illlll the \]){,l{'(,lllf/l~},t , o\[ ('{}IT{'(I (htssilicatilllt iv} l't'l}Ol't i'd.</Paragraph>
    <Paragraph position="3">  l)\]es. We gaY(-`, }towevtw~ air (!a-qy Way to &amp;quot;(:()m\])il(C/' the relewmt backgrouiM knowh!dge along with th(! ambignous training examl)h!s into a modilied set o\[ training examph!s on which w,! were abh! to directly run 11)3. Experiments comparing these approachC/,s showed that the rules learned using the second ap preach with the ambiguity present in the training cx3.Ittpl(!s are ahttost as 3.ccltt*~ttt! ils those ()})tltill(!d fl'ollI arnlfignity-free examples using llaussh'r's alg(n'ithnL Ow.'rall, our experiments sho~ed that using Iliachine learning techniques yiehls ruh!s that are highly itct:llrltte (:otllpared to the ttuttntally created rules.</Paragraph>
    <Paragraph position="4"> These results suggest that exploiting the reported inductiw. * lem'ning techniques will significantly accehq'ate the construction process of AIJI'-J/E's translation ruh.'s. Currently, the reported learning aplnoachos are I)eing inchlded in at semi-imtonmtic knowledge aC(luisition tool to be ttsc(l ill the actual (leveh)im,ont of the AUI'-J/F system.</Paragraph>
    <Paragraph position="5"> Acknowledgelnent: \Ve wish to thank l)r. S.</Paragraph>
    <Paragraph position="6"> lkehara for his COlltiitllOllS (!ttc()ltrilg{~Itlcllt. This work W~LS done while the first author was spending a I)()stdoctoral yem. at NTT. lle Mso thanks King l&amp;quot;ahd Unw~rsity of Petrohmm and Minerals, Saudi Arabia, for their support.</Paragraph>
  </Section>
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