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<Paper uid="W05-1004">
  <Title>Automatically Learning Qualia Structures from the Web</Title>
  <Section position="6" start_page="33" end_page="34" type="relat">
    <SectionTitle>
5 Related Work
</SectionTitle>
    <Paragraph position="0"> There is quite a lot of work related to the use of linguistic patterns to discover certain ontological relations from text. Hearst's (Hearst, 1992) seminal work had the aim of discovering taxonomic relations from electronic dictionaries. The precision of the is-a-relations learned is 61/106 (57.55%) when measured against WordNet as gold standard, which is comparable to our results.</Paragraph>
    <Paragraph position="1"> Hearst's idea has been reapplied by different researchers with either slight variations in the patterns used (Iwanska et al., 2000), to acquire knowledge for anaphora resolution (Poesio et al., 2002), or to discover other kinds of semantic relations such as part-of relations (Charniak and Berland, 1999) or causation relations (Girju and Moldovan, 2002).</Paragraph>
    <Paragraph position="2"> Instead of matching these patterns in a large text collection, some researchers have recently turned to the Web to match these patterns such as in (Cimiano and Staab, 2004) or (Markert et al., 2003). (Cimiano and Staab, 2004) for example aim at learning instance-of as well as taxonomic (is-a) relations. This is very related to the acquisition of the Formal role proposed here. (Markert et al., 2003) aim at acquiring knowledge for anaphora resolution, while (Etzioni et al., 2004) aim at learning the complete extension of a certain concept. For example, they aim at finding all the actors in the world.</Paragraph>
    <Paragraph position="3"> Our approach goes further in that it not only learns typing, superconcept or instance-of relations, but also Constitutive and Telic relations.</Paragraph>
    <Paragraph position="4"> There also exist approaches specifically aiming at learning qualia elements from corpora based on machine learning techniques. (Claveau et al., 2003) for example use Inductive Logic Programming to learn if a given verb is a qualia element or not. However, their approach goes not as far as learning the complete qualia structure for a lexical element in an unsupervised way as presented in our approach. In fact, in their approach they do not distinguish between different qualia roles and restrict themselves to verbs as potential fillers of qualia roles. (Yamada and Baldwin, 2004) present an approach to learning Telic and Agentive relations from corpora analyzing two different approaches: one relying on matching certain lexico-syntactic patterns as in the work presented here, but also a second approach consisting in training a maximum entropy model classifier. Their conclusion is that the results produced by the classification approach correlate better with two hand-crafted gold standards.</Paragraph>
    <Paragraph position="5">  The patterns used by (Yamada and Baldwin, 2004) differ substantially from the ones used in this paper, which is mainly due to the fact that search engines do not provide support for regular expressions and thus instantiating a pattern as 'V[+ing] Noun' is impossible in our approach as the verbs are unknown a priori.</Paragraph>
    <Paragraph position="6"> Finally, (Pustejovsky et al., 1993) present an interesting framework for the acquisition of semantic relations from corpora not only relying on statistics, but guided by theoretical lexicon principles.</Paragraph>
  </Section>
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