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<Paper uid="H05-1113">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 899-906, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Measuring the relative compositionality of verb-noun (V-N) collocations by integrating features</Title>
  <Section position="8" start_page="903" end_page="904" type="evalu">
    <SectionTitle>
8 Experiments and Results
</SectionTitle>
    <Paragraph position="0"> For training, we used 10% of the data and for testing, we use 90% of the data as the goal is to use only a small portion of the data for training (Data was divided in 10 different ways for cross-validation. The results presented here are the average results).</Paragraph>
    <Paragraph position="1"> All the statistical measures show that the expressions ranked higher according to their decreasing values are more likely to be non-compositional. We compare these ranks with the human rankings (obtained using the average ratings of the users). To compare, we use Pearson's Rank-Order Correlation Coefficient (a12 a13 ) (Siegel and Castellan, 1988).</Paragraph>
    <Paragraph position="2"> We integrate all the seven features using the SVM based ranking function (described in section 7). We  see that the correlation between the relative compositionality of the V-N collocations computed by the SVM based ranking function is significantly higher than the correlation between the individual features and the human ranking (Table 3).</Paragraph>
    <Paragraph position="3">  individual features and the ranking of SVM based ranking function with the ranking of human judgements null In table 3, we also see that the contextual feature which we proposed, 'Similarity of the collocation to the verb-form of the object' (a44 ), correlated significantly higher than the other features which indicates that it is a good measure to represent the semantic compositionality of V-N expressions. Other expressions which were good indicators when compared to the traditional features are 'Least mutual information difference with similar collocations' (a38 ) and  added to the ranking function To observe the contribution of the features to the SVM based ranking function, we integrate the features (section 6) one after another (in two different ways) and compute the relative order of the collocations according to their compositionality. We see that as we integrate more number of relevant compositionality based features, the relative order correlates better (better a12 a13 value) with the human ranking (Figure 1). We also see that when the feature 'Least mutual information difference with similar collocations' is added to the SVM based ranking function, there is a high rise in the correlation value indicating it's relevance. In figure 1, we also observe that the context-based features did not contribute much to the SVM based ranking function even though they performed well individually.</Paragraph>
  </Section>
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