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<Paper uid="N06-2004">
  <Title>Measuring Semantic Relatedness Using People and WordNet</Title>
  <Section position="7" start_page="14" end_page="15" type="concl">
    <SectionTitle>
6 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We proposed a dataset of relatedness judgements that differs from the existing ones9 in (1) size about 7000 items, as opposed to up to 350 in existing datasets; (2) cross-POS data, as opposed to purely nominal or verbal; (3) a broad approach to semantic relatedness, not focussing on any particular relation, but grounding it in the reader's (idea of) common knowledge; this as opposed to synonymy-based similarity prevalent in existing databases.</Paragraph>
    <Paragraph position="1"> We explored the new data with WordNet-based measures, showing that (1) the data is different in character from a standard similarity dataset, and very challenging for state-of-the-art methods; (2) the proposed novel WordNet-based measure of relatedness usually outperforms its competitor, as well as a state-of-the-art similarity measure when the latter applies.</Paragraph>
    <Paragraph position="2"> In future work, we plan to explore distributional methods for modeling relatedness, as well as the use of text-based information to improve correlations with the human data, as judgments are situated in specific textual contexts.</Paragraph>
  </Section>
class="xml-element"></Paper>