<?xml version="1.0" standalone="yes"?>
<Paper uid="P06-1117">
  <Title>Semantic Role Labeling via FrameNet, VerbNet and PropBank</Title>
  <Section position="3" start_page="0" end_page="929" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> During the last years a noticeable effort has been devoted to the design of lexical resources that can provide the training ground for automatic semantic role labelers. Unfortunately, most of the systems developed until now are confined to the scope of the resource used for training. A very recent example in this sense was provided by the CONLL 2005 shared task (Carreras and M`arquez, 2005) on PropBank (PB) (Kingsbury and Palmer, 2002) role labeling. The systems that participated in the task were trained on the Wall Street Journal corpus (WSJ) and tested on portions of WSJ and Brown corpora. While the best F-measure recorded on WSJ was 80%, on the Brown corpus, the F-measure dropped below 70%. The most significant causes for this performance decay were highly ambiguous and unseen predicates (i.e.</Paragraph>
    <Paragraph position="1"> predicates that do not have training examples).</Paragraph>
    <Paragraph position="2"> The same problem was again highlighted by the results obtained with and without the frame information in the Senseval-3 competition (Litkowski, 2004) of FrameNet (Johnson et al., 2003) role labeling task. When such information is not used by the systems, the performance decreases by 10 percent points. This is quite intuitive as the semantics of many roles strongly depends on the focused frame. Thus, we cannot expect a good performance on new domains in which this information is not available.</Paragraph>
    <Paragraph position="3"> A solution to this problem is the automatic frame detection. Unfortunately, our preliminary experiments showed that given a FrameNet (FN) predicate-argument structure, the task of identifying the associated frame can be performed with very good results when the verb predicates have enough training examples, but becomes very challenging otherwise. The predicates belonging to new application domains (i.e. not yet included in FN) are especially problematic since there is no training data available.</Paragraph>
    <Paragraph position="4"> Therefore, we should rely on a semantic context alternative to the frame (Giuglea and Moschitti, 2004). Such context should have a wide coverage and should be easily derivable from FN data. A very good candidate seems to be the Intersective Levin class (ILC) (Dang et al., 1998) that can be found as well in other predicate resources like PB and VerbNet (VN) (Kipper et al., 2000).</Paragraph>
    <Paragraph position="5"> In this paper we have investigated the above claim by designing a semi-automatic algorithm that assigns ILCs to FN verb predicates and by carrying out several semantic role labeling (SRL) experiments in which we replace the frame with the ILC information. We used support vector ma- null chines (Vapnik, 1995) with (a) polynomial kernels to learn the semantic role classification and (b) Tree Kernels (Moschitti, 2004) for learning  both frame and ILC classification. Tree kernels were applied to the syntactic trees that encode the subcategorization structures of verbs. This means that, although FN contains three types of predicates (nouns, adjectives and verbs), we only concentrated on the verb predicates and their roles. The results show that: (1) ILC can be derived with high accuracy for both FN and Probank and (2) ILC can replace the frame feature with almost no loss in the accuracy of the SRL systems. At the same time, ILC provides better predicate coverage as it can also be learned from other corpora (e.g.</Paragraph>
    <Paragraph position="6"> PB).</Paragraph>
    <Paragraph position="7"> In the remainder of this paper, Section 2 summarizes previous work done on FN automatic role detection. It also explains in more detail why models based exclusively on this corpus are not suitable for free-text parsing. Section 3 focuses on VN and PB and how they can enhance the robustness of our semantic parser. Section 4 describes the mapping between frames and ILCs whereas Section 5 presents the experiments that support our thesis. Finally, Section 6 summarizes the conclusions. null</Paragraph>
  </Section>
class="xml-element"></Paper>