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<Paper uid="W06-1518">
  <Title>Using LTAG-Based Features for Semantic Role Labeling</Title>
  <Section position="6" start_page="130" end_page="130" type="evalu">
    <SectionTitle>
4 Experiments and Results
</SectionTitle>
    <Paragraph position="0"> We use the PropBank corpus of predicate-argument structures (Palmer, Gildea and Kingsbury, 2005) as our source of annotated data for the  using gold standard parse trees. M1 is the LTAG-based model, M2 is the derived tree pattern matching Model, M3 is a hybrid model SRL task. However, there are many different ways to evaluate performance on the PropBank, leading to incomparable results. To avoid such a situation, in this paper we use the CoNLL 2005 shared SRL task data (Carreras and M`arquez, 2005) which provides a standard train/test split, a standard method for training and testing on various problematic cases involving coordination. However, in some cases, the CoNLL 2005 data is not ideal for the use of LTAG-based features as some &amp;quot;deep&amp;quot; information cannot be recovered due to the fact that trace information and other empty categories like PRO are removed entirely from the training data.</Paragraph>
    <Paragraph position="1"> As a result some of the features that undo long-distance movement via trace information in the TreeBank as used in (Chen and Rambow, 2003) cannot be exploited in our model. Our results are shown in Table 1. Note that we test on the gold standard parse trees because we want to compare a model using features from the derived parse trees to the model using the LTAG derivation trees.</Paragraph>
  </Section>
class="xml-element"></Paper>