<?xml version="1.0" standalone="yes"?>
<Paper uid="P06-2041">
  <Title>Discriminative Classifiers for Deterministic Dependency Parsing</Title>
  <Section position="8" start_page="321" end_page="321" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have performed an empirical comparison of MBL (TIMBL) and SVM (LIBSVM) as learning methods for classifier-based deterministic dependency parsing, using data from three languages and feature models of varying complexity. The evaluation shows that SVM gives higher parsing accuracy and comparable or better parsing efficiency for complex, lexicalized feature models across all languages, whereas MBL is superior with respect to training efficiency, even if training data is divided into smaller sets for SVM. The best accuracy obtained for SVM is close to the state of the art for all languages involved.</Paragraph>
  </Section>
class="xml-element"></Paper>