<?xml version="1.0" standalone="yes"?>
<Paper uid="H94-1025">
  <Title>Building Japanese-English Dictionary based on Ontology for Machine Translation</Title>
  <Section position="6" start_page="144" end_page="145" type="concl">
    <SectionTitle>
5. Discussion
</SectionTitle>
    <Paragraph position="0"> In order to get better results, we are now improving the ratio of the open words and the close words from the following three viewpoints.</Paragraph>
    <Paragraph position="1">  1. Semantic distance measurement  To reduce the number of open words, the example match is being improved by using a more sophisticated algorithm for the semantic distance measured in the ontology\[Resnik, 1993; Knight, 1993\]. This measurement is also useful for improving the argument match, because the argument constraints are often described by the specific examples. In this case, the semantic distance measurement algorithm helps to determine whether the bilingual argument constraints are identical with the ontology argument constraints or not.</Paragraph>
    <Paragraph position="2"> 2. Other lexicons and databases For further improvement, other lexicons should be exploited. The open words usually are high ambiguity words with little information in the bilingual dictionary that have one equivalent English word with many meanings, with little constraint information and few examples. To compensate for the lack of information, we are now referring to other bilingual dictionaries and Japanese lexicons.</Paragraph>
    <Paragraph position="3"> 3. Integration of the three algorithms To reduce the number of close words, one integrated algorithm is being designed. By using the semantic distance measurement algorithm, one matching degree can be defined for both argument match and example match. Though the current equivalent-word match provides a high confidence only when all English-equivalent words share ontology concepts, we define the matching degree according to the number of English-equivalent words which can share ontology concepts. For example, when two of three English-equivalent words share an ontology concept EW~j_I_I and the other English-equivalent word is linked to an ontology concept EWkj-2-1, a matching degree 0.66 is assigned to the association with EWkj _1_1, and a matching degree 0.33 to EWkj ..2_1.  (the sloping side of a declivity containing a large body of water) (a long ridge or pile; &amp;quot;a bank of earth&amp;quot;) depository financial institution (a financial institution that accepts deposits and channels the money into lending activities) array (an arrangement of aerials spaced to give desired directional characteristics) Figure 12: Ontology concepts and definitions for &amp;quot;bank&amp;quot; We determine the optimal weights for the .three matching degrees based on the data used for simulation so that the integration algorithm can provide the most plausible association for the open words.</Paragraph>
    <Paragraph position="4"> As well as improving these points, we are applying the algorithms to more words and other parts of speech. We plan to apply the algorithms to other bilingual dictionaries such as Chinese-English in order to increase the sophistication of the ontology for our multilingual MT system.</Paragraph>
  </Section>
class="xml-element"></Paper>