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<Paper uid="C00-1002">
  <Title>Learning Word Clusters from Data Types</Title>
  <Section position="6" start_page="12" end_page="13" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We described a linguistic knowledge acquisition model and tested it; on a word classification task.</Paragraph>
    <Paragraph position="1"> The main points of our prot)osal are: * classification is asymmetric, grounded on principles of machine learning with intinite memory; * the algorithm is explorative and nonreductionist; no a priori model of class dis- null collocates of polysemous verbs.</Paragraph>
    <Paragraph position="2"> tril)ution is assmned; * classification is modelled z~s the task of forming a web of context dependent semantic associations among words; * the approach uses a context--sensitive notion of semantic similarity; * the approach rests on the notion of analogical proportion, which proves to t)e a reliable intbrmation refit for measuring semantic similarity; * analogical t)roportions are harder to track down than simple pairs, and interconnected in a highly complex way; yet, reliance on data types, as opposed to token frequencies, makes the proposed method comtmrationally tractable and resistant to data sparseness.</Paragraph>
  </Section>
class="xml-element"></Paper>