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<Paper uid="P06-2064">
  <Title>Interpreting Semantic Relations in Noun Compounds via Verb Semantics</Title>
  <Section position="12" start_page="494" end_page="495" type="evalu">
    <SectionTitle>
7 Evaluation
</SectionTitle>
    <Paragraph position="0"> We evaluated our method over both 17 semantic relations(withoutEQUATIVEandTIME)andthefull 19 semantic relations, due to the low frequency and lack of verb-based constructional contexts for EQUATIVE and TIME, as indicated in Table 2. Note that the test data set is the same for both sets of semantic relations, but that the training data in the case of 17 semantic relations will not contain any instances for the EQUATIVE and TIME relations, meaning that all such test instances will be misclassified. The baseline for all verb mapping methods is a simple majority-class classifier, which leads to an accuracy of 42.4% for the TOPIC relation. In evaluation, we use two different values for our method: Count and Weight. Count is based on the raw number of corpus instances, while Weight employs the seed verb weight described in Section 6.1.</Paragraph>
    <Paragraph position="1"> 4There is only one instance of a seed verb mapping to multiple semantic relations, namely perform which corresponds to the two relations AGENT and OBJECT.</Paragraph>
    <Paragraph position="2">  As noted above, we excluded all NCs for which we were unable to find at least 5 instances of the modifier and head noun in an appropriate sentential context. This exclusion reduced the original set of 2,166 NCs to only 453, meaning that the proposedmethodisunabletoclassifyupto80%of NCs. For real-world applications, a method which is only able to arrive at a classification for 20% of instances is clearly of limited utility, and we need some way of expanding the coverage of the proposed method. This is achieved by adapting the similarity method proposed by Kim and Baldwin (2005) to our task, wherein we use lexical similarity to identify the nearest-neighbour NC for a given NC, and classify the given NC according to the classification for the nearest-neighbour. The results for the combined method are presented in</Paragraph>
  </Section>
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