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<Paper uid="P03-2029">
  <Title>Word Sense Disambiguation Using Pairwise Alignment</Title>
  <Section position="5" start_page="0" end_page="0" type="evalu">
    <SectionTitle>
4 Experimental Result
</SectionTitle>
    <Paragraph position="0"> Up to the present, we have obtained the experimental results on 7 verbs in SENSEVAL-13. In our experiment, for all sentences including target word in the training and test corpus of SENSEVAL-1, we make a parsing using Apple Pie Parser (Sekine, 1996) and additional vertices using some rules automatically. If the resulted parsing includes some errors, we remove them by hand. Then we obtain the sequence patterns by hand from training data and attempt WSD using equation (1) for test data. Because of various length of sequence, we assign score zero to the preceding and right-end gaps in an alignment.</Paragraph>
    <Paragraph position="1"> We show our experimental results in Table 1. In SENSEVAL-1, precisions and recalls are calculated by three scoring ways, fine-grained, mixed-grained and coarse-grained scoring. We show the results only by fine-grained scoring which is evaluated by distinguishing word sense in the strictest way. It is impossible to make simple comparison with the participants in SENSEVAL-1 because our method needs supervised learning by hand. However, 2.8%14.2% improvements of the accuracy compared with the best system seems significant, suggesting that our method is promising for WSD.</Paragraph>
  </Section>
class="xml-element"></Paper>