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<Paper uid="I05-2033">
  <Title>POS tagger combinations on Hungarian text</Title>
  <Section position="6" start_page="194" end_page="195" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper we investigated the possibility of improving the classi cation performance of POS taggers by applying classi er combination schemas. For the experiments we chose TBL as a tagger and Bagging and Boosting as combination schemas. The results indicates that Bagging and Boosting can reduce the classi cation error of the TBL tagger by 18% and 24.7%, respectively.</Paragraph>
    <Paragraph position="1"> It is clear that further improvements could be made by tailoring the tagger algorithm to the requirements of more sophisticated boosting methods like Adaboost.M2 and other derivatives optimized for multi-class recognition. Another promising idea for more e ective combinations is that of applying con dence-type generative (ANN, kNN, SVM) or discriminative (HMM) learners (Duda et al., 2001; Bishop, 1995; Vapnik, 1998). These kinds of classi ers provide more information about the recognition results and can improve the cooperation between the combiner and the classi ers. In the future we plan to investigate these alternatives for constructing better tagger combinations. null</Paragraph>
  </Section>
class="xml-element"></Paper>