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<Paper uid="W04-0858">
  <Title>Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities</Title>
  <Section position="4" start_page="0" end_page="0" type="evalu">
    <SectionTitle>
3 Results
</SectionTitle>
    <Paragraph position="0"> A total of 26 teams entered 47 systems (both supervised and unsupervised) in the Senseval-3 ELS task. Table 2 compares the fine-grained and  coarse-grained scores of our four entries with other Senseval-3 systems.</Paragraph>
    <Paragraph position="1"> With NRC-Fine and NRC-Coarse, each semantic feature was scored by calculating its PMI with the head word, and then low scoring semantic features were dropped. With NRC-Fine2 and NRC-Coarse2, the threshold for dropping features was changed, so that many more features were retained. The Senseval-3 results suggest that it is better to drop more features.</Paragraph>
    <Paragraph position="2"> NRC-Coarse and NRC-Coarse2 were designed to maximize the coarse score, by training them with data in which the senses were relabeled by their coarse sense equivalence classes. The fine scores for these two systems are meaningless and should be ignored. The Senseval-3 results indicate that there is no advantage to relabeling.</Paragraph>
    <Paragraph position="3"> The NRC systems scored roughly midway between the best and median systems. This performance supports the hypothesis that corpus-based semantic features can be useful for WSD. In future work, we plan to design a system that combines corpus-based semantic features with the most effective elements of the other Senseval-3 systems. For reasons of computational efficiency, we chose a relatively narrow window of nine-words around the head word. We intend to investigate whether a larger window would bring the system performance up to the level of the best Senseval-3 system.</Paragraph>
  </Section>
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