<?xml version="1.0" standalone="yes"?>
<Paper uid="W06-1649">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Supervised and semi-supervised sense disambiguation methods will mis-tag the instances of a target word if the senses of these instances are not de ned in sense inventories or there are no tagged instances for these senses in training data. Here we used a model order identi cation method to avoid the misclassi cation of the instances with unde ned senses by discovering new senses from mixed data (tagged and untagged corpora). This algorithm tries to obtain a natural partition of the mixed data by maximizing a stability criterion de ned on the classi cation result from an extended label propagation algorithm over all the possible values of the number of senses (or sense number, model order). Experimental results on SENSEVAL-3 data indicate that it outperforms SVM, a one-class partially supervised classi cation algorithm, and a clustering based model order identi cation algorithm when the tagged data is incomplete. null</Paragraph>
  </Section>
class="xml-element"></Paper>