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<Paper uid="P04-1080">
  <Title>Learning Word Senses With Feature Selection and Order Identification Capabilities</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents an unsupervised word sense learning algorithm, which induces senses of target word by grouping its occurrences into a &amp;quot;natural&amp;quot; number of clusters based on the similarity of their contexts. For removing noisy words in feature set, feature selection is conducted by optimizing a cluster validation criterion subject to some constraint in an unsupervised manner. Gaussian mixture model and Minimum Description Length criterion are used to estimate cluster structure and cluster number.</Paragraph>
    <Paragraph position="1"> Experimental results show that our algorithm can find important feature subset, estimate model order (cluster number) and achieve better performance than another algorithm which requires cluster number to be provided.</Paragraph>
  </Section>
class="xml-element"></Paper>