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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="2" start_page="0" end_page="24" type="abstr">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Lakoff (1993) argues that rather than being a rare form of creative language, some metaphors are ubiquitous, highly structured, and relevant to cognition. To date, there has been no robust, broadly applicable computational metaphor interpretation system, a gap this article is intended to take a first step toward filling.</Paragraph>
    <Paragraph position="1"> Most computational models of metaphor depend on hand-coded knowledge bases and work on a few examples. CorMet is designed to work on a larger class of metaphors by extracting knowledge from large corpora without drawing on any hand-coded knowledge sources besides WordNet.</Paragraph>
    <Paragraph position="2"> A method for computationally interpreting metaphorical language would be useful for NLP. Although metaphorical word senses can be cataloged and treated as just another part of the lexicon, this kind of representation ignores regularities in polysemy. A conventional metaphor may have a very large number of linguistic manifestations, which makes it useful to model the metaphor's underlying mechanisms. CorMet is not capable of interpreting any manifestation of conventional metaphor but is a step toward such a system.</Paragraph>
    <Paragraph position="3"> CorMet analyzes large corpora of domain-specific documents and learns the selectional preferences of the characteristic verbs of each domain. A selectional preference is a verb's predilection for a particular type of argument in a particular role. For instance, the object of the verb pour is generally a liquid. Any noun that pour takes as an  [?] Computer Science Department, Waltham, MA 02134. E-mail: zmason@amazon.com.</Paragraph>
    <Paragraph position="4">  Computational Linguistics Volume 30, Number 1 an object is likely to be intended as a liquid, either metaphorically or literally. CorMet finds conventional metaphors by finding systematic differences in selectional preferences between domains. For instance, if CorMet were to find a sentence like Funds poured into his bank account in a document from the FINANCE domain, it could infer that in that domain, pour has a selection preference for financial assets in its subject. By comparing this selectional preference with pour's selectional preferences in the LAB domain, CorMet can infer a metaphorical mapping from money to liquids. By finding sets of co-occuring interconcept mappings (like the above mapping and a mapping from investments to containers, for instance), Cormet can articulate the higher-order structure of conceptual metaphors. Note that Cormet is designed to detect higher-order conceptual metaphors by finding some of the sentences embodying some of the interconcept mappings constituting the metaphor of interest but is not designed to be a tool for reliably detecting all instances of a particular metaphor.</Paragraph>
    <Paragraph position="5"> CorMet's domain-specific corpora are obtained from the Internet. In this context, a domain is a set of related concepts, and a domain-specific corpus is a set of documents relevant to those concepts. CorMet's input parameters are two domains between which to search for interconcept mappings and, for each domain, a set of characteristic keywords.</Paragraph>
    <Paragraph position="6"> CorMet is tested on its ability to find a subset of the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991), a manually compiled catalog of metaphor. CorMet works on domains that are specific and concrete (e.g., the domain of finance, but not that of actions). CorMet's discrimination is relatively coarse: It measures trends in selectional preferences across many documents, so common mappings are discernible.</Paragraph>
    <Paragraph position="7"> CorMet considers the selectional preferences only of verbs, on the theory that they are generally more selectively restrictive than nouns or adjectives.</Paragraph>
    <Paragraph position="8"> It is worth noting that WordNet, CorMet's primary knowledge source, implicitly encodes some of the metaphors CorMet is intended to find; Peters and Peters (2000) use WordNet to find many artifact/cognition metaphors. Also, WordNet enumerates some metaphorical senses of some verbs. CorMet does not use any of WordNet's information about verbs and ignores regularities in the distribution of noun homonyms that could be used to find some metaphors.</Paragraph>
    <Paragraph position="9"> The article is organized as follows: Section 2 describes the mechanisms by which conventional metaphors are detected. Section 3 walks through CorMet's process in two examples. Section 4 describes how the system's performance is evaluated against the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991), and Section 5 covers select related work.</Paragraph>
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