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<Paper uid="P97-1031">
  <Title>A Flexible POS Tagger Using an Automatically Acquired Language Model*</Title>
  <Section position="9" start_page="243" end_page="243" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented an automatic constraint learning algorithm based on statistical decision trees.</Paragraph>
    <Paragraph position="1"> We have used the acquired constraints in a part-of-speech tagger that allows combining any kind of constraints in the language model.</Paragraph>
    <Paragraph position="2"> The results obtained show a clear improvement in the performance when the automatically acquired constraints are added to the model. That indicates that relaxation labelling is a flexible algorithm able to combine properly different information kinds, and that the constraints acquired by the learning algorithm capture relevant context information that was not included in the n-gram models.</Paragraph>
    <Paragraph position="3"> It is difficult to compare the results to other works, since the accuracy varies greatly depending on the corpus, the tag set, and the lexicon or morphological analyzer used. The more similar conditions reported in previous work are those experiments performed on the WSJ corpus: (Brill, 1992) reports 3-4% error rate, and (Daelemans et al., 1996) report 96.7% accuracy. We obtained a 97.39% accuracy with tri-grams plus automatically acquired constraints, and 97.45% when hand written constraints were added.</Paragraph>
  </Section>
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