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<Paper uid="W98-0701">
  <Title>I I I I I I 1 General Word Sense Disambiguation Method Based on a Full Sentential Context</Title>
  <Section position="9" start_page="5" end_page="7" type="relat">
    <SectionTitle>
8 Related Work
</SectionTitle>
    <Paragraph position="0"> To our knowledge, there is no current method which attempts to identify the senses of all words in whole  ! ! sentences, so we cannot make a practical comparison. null Similarly to our work, (Resnik, 1995)(Agirre and Rigau, 1996) challenge the fine-grainedness of Word-Net, but their work is limited to nouns only. (Agirre and Rigau, 1996) report coverage 86.2%, precision 71.2% and recall 61.4% for nouns in four randomly selected semantic concordance files. From among the methods based on semantic distance, (Reanik, 1993)(Sussna, 1993) use a similar semantic distance measure for two concepts in WordNet, but they also focus on selected group of nouns only. (Karov and Edelman, 1996) use an interesting iterative algorithm and attempt to solve the sparse data bottleneck by using a graded measure of contextual similarity. They achieve 90.5, 92.5, 94.8 and 92.3 percent accuracy in distinguishing between two senses of the noun drug, sentence, suit and player, respectively. (Yarowsky, 1995), whose training corpus for the noun drug was 9 times bigger than that of Karov and Edelman, reports 91.4% correct performance improved to impressive 93.9% when using the &amp;quot;one sense per discourse&amp;quot; constraint. These methods, however, focus on only two senses of a very limited number of nouns and therefore are not comparable with our approach.</Paragraph>
  </Section>
class="xml-element"></Paper>