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<Paper uid="J04-1002">
  <Title>c(c) 2004 Association for Computational Linguistics CorMet: A Computational, Corpus-Based Conventional Metaphor Extraction System</Title>
  <Section position="7" start_page="42" end_page="43" type="relat">
    <SectionTitle>
5. Related Work
</SectionTitle>
    <Paragraph position="0"> Two of the most broadly effective computational models of metaphor are Fass (1991) and Martin (1990), in both of which metaphors are detected through selectional-preference violations and interpreted using an ontology. They are distinguished from CorMet in that they work on both novel and conventional metaphors and rely on declarative hand-coded knowledge bases.</Paragraph>
    <Paragraph position="1"> Fass (1991) describes Met*, a system for interpreting nonliteral language that builds on Wilks (1975) and Wilks (1978). Met* discriminates among metonymic, metaphorical, literal, and anomalous language. It is a component of collative semantics, a semantics for natural language processing that has been implemented in the program meta5 (Fass, 1986, 1987, 1988). Met* treats metonymy as a way of referring to one thing by means of another and metaphor as a way of revealing an interesting relationship between two entities.</Paragraph>
    <Paragraph position="2"> In Met*, a verb's selectional preferences are represented as a vector of types. The verb drink's preference for an animal subject and a liquid object are represented as (animal, drink, liquid). Metaphorical interpretations are made by finding a sense vector in Met*'s knowledge base whose elements are hypernyms of both the preferred argument types and the actual arguments. For example, the car drinks gasoline maps to the vector (car, drink, gasoline). But car is not a hypernym of animal, so Met* searches for a metaphorical interpretation, coming up with (thing, use, energy source).</Paragraph>
    <Paragraph position="3"> Martin (1990) describes the Metaphor Interpretation, Denotation, and Acquisition System (MIDAS), a computational model of metaphor interpretation. MIDAS has been integrated with the Unix Consultant (UC), a program that answers English questions about using Unix. UC tries to find a literal answer to each question with which it is presented. If violations of literal selectional preference make this impossible, UC calls on MIDAS to search its hierarchical library of conventional metaphors for one that explains the anomaly. If no such metaphor is found, MIDAS tries to generalize a known conventional metaphor by abstracting its components to the most-specific senses that encompass the question's anomalous language. MIDAS then records the</Paragraph>
    <Section position="1" start_page="43" end_page="43" type="sub_section">
      <SectionTitle>
Mason CorMet
</SectionTitle>
      <Paragraph position="0"> most concrete metaphor descended from the new, general metaphor that provides an explanation for the query's language.</Paragraph>
      <Paragraph position="1"> MIDAS is driven by the idea that novel metaphors are derived from known, existing ones. The hierarchical structure of conventional metaphor is a regularity not captured by other computational approaches. Although MIDAS can quickly understand novel metaphors that are the descendants of metaphors in its memory, it cannot interpret compound metaphors or detect intermetaphor relationships besides inheritance. INVESTMENTS - CONTAINERS and MONEY - WATER, for instance, are clearly related, but not in a way that MIDAS can represent. Since not all novel metaphors are descendants of common conventional metaphors, MIDAS's coverage is limited.</Paragraph>
      <Paragraph position="2"> MetaBank (Martin 1994) is an empirically derived knowledge base of conventional metaphors designed for use in natural language applications. MetaBank starts with a knowledge base of metaphors based on the Master Metaphor List. MetaBank can search a corpus for one metaphor or scan a large corpus for any metaphorical content.</Paragraph>
      <Paragraph position="3"> The search for a target metaphor is accomplished by choosing a set of probe words associated with that metaphor and finding sentences with those words, which are then manually sorted as literal, examples of the target metaphor, examples of a different metaphor, unsystematic homonyms, or something else. MetaBank compiles statistics on the frequency of conventional metaphors and the usefulness of the probe words.</Paragraph>
      <Paragraph position="4"> MetaBank has been used to study container metaphors in a corpus of UNIX-related e-mail and to study metaphor distributions in the Wall Street Journal.</Paragraph>
      <Paragraph position="5"> Peters and Peters (2000) mine WordNet for patterns of systematic polysemy by finding pairs of WordNet nodes at a relatively high level in the ontology (but still below the root nodes) whose descendants share a set of common word forms. The nodes publication and publisher, for instance, have paper, newspaper, and magazine as common descendants. This is a metonymic relationship; the system can also capture metaphoric relationships, as in the nodes supporting structure and theory, among whose common descendants are (for example) framework, foundation, and base. Peters and Peters' system found many metaphoric relationships between node pairs that were descendants of the unique beginners artifact and cognition.</Paragraph>
      <Paragraph position="6"> Goatly (1997) describes a set of linguistic cues of metaphoricality beyond selectional-preference violations, such as metaphorically speaking and, surprisingly, literally. These cues are generally ambiguous (except for metaphorically speaking) but could usefully be incorporated into computational approaches to metaphor.</Paragraph>
    </Section>
  </Section>
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