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<Paper uid="W04-1908">
  <Title>Automated Induction of Sense in Context</Title>
  <Section position="5" start_page="0" end_page="0" type="evalu">
    <SectionTitle>
4 Results and Discussion
</SectionTitle>
    <Paragraph position="0"> The experimental trials performed to date are too preliminary to validate the methodology outlined above in general terms for the WSD task. Our results are encouraging however, and comparable to the best performing systems reported from Senseval 2. For our experiments, we implemented two machine learning algorithms, instance-based k-Nearest Neighbor, and a decision tree algorithm (a version of ID3). Table 2 shows the results on a subset of verbs that have been processed, also listing the number of patterns in the pattern set for each of the verbs.2 verb number of training accuracy patterns set ID3 kNN edit 2 100 87% 86% treat 4 200 45% 52% submit 4 100 59% 64%  Further experimentation is obviously needed to adequately gauge the e ectiveness of the selection context approach for WSD and other NLP tasks. It is already clear, however, that the traditional sense enumeration approach, where senses are associated with individual lexical items, must give way to a model where senses are assigned to the contexts within which words appear. Furthermore, because the variability of the stereotypical syntagmatic patterns that are associated with words appears to be relatively small, such information can be encoded as lexically-indexed contexts. A comprehensive dictionary of such contexts could prove to be a powerful tool for a variety of NLP tasks.</Paragraph>
  </Section>
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