Metaphor in Terminology 267and must thus have an alternative source of motivation, unaccounted for
by the CMT. Driven by this kind of incongruence, Evans (2013) proposes
a distinction between figurative language based on conceptual metaphors
as defined by Lakoff and Johnson (1980) and figurative language
motivated by what he labels discourse metaphor. While the former are
“independent of language but influence certain types of language use, [...]
discourse metaphors are linguistically mediated instances of figurative
language use. While they presumably have a conceptual basis, they arise
in language use to address particular and often specific communicative
needs and functions” (Evans 2013: 75). Unlike conceptual metaphors,
discourse metaphors can be based on various kinds of resemblance and are
also influenced by language use: they are capable of “tak[ing] on more
abstract semantic functions than those they were originally employed to
express. [...] As they become better established, they appear to take on a
more generic meaning, which corresponds to them becoming more
entrenched” (Evans 2013: 83).
Whereas Evans (2013) seems to base his distinction on the analysis of
metaphors in poetic language, we believe that the construct of discourse
metaphor can be effectively applied to cases of figurative language in
specialized discourse and terminology, as these tend to involve many
instances of metaphorical mappings between tangible domains, and, in
addition, are characterized by specific communicative needs, particularly
the transfer of specialized knowledge in the most effective manner
possible. In the following section, we demonstrate one such group of
mappings in specialized language and explain how it can be interpreted as
a case of discourse metaphor.
Metaphor in specialized language:
MACHINE IS A HUMAN BODY
In the following section, we use examples from a self-compiled corpus of
specialized language and examine metaphorical mappings from the
domain of HUMAN BODY onto the domain of HEAVY MACHINERY.^3 As the
relationship between the domains of BODY and MACHINE seems to be very
productive with respect to the span and occurrences of metaphorical
(^3) The corpus is stored and processed using the Sketch Engine (Kilgariff et al.
2014). The corpus has 1,624, 867 tokens and includes texts (operation manuals,
promotion materials, specialized articles and tutorials) from the field of heavy
machinery, mostly machines used in agriculture, forestry and construction, as well
as automotive technology. See Králiková (2015) for the details of the research.