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A Comparative Corpus-driven study of Animation Metaphor in Native and Non-
native Student Writing
Claudia Marcela Chapetón
Universidad Pedagógica Nacional, Bogotá / University of Barcelona1
Abstract
Taking an applied linguistic approach to the study of metaphor, this comparative
corpus-driven study looks at the realizations of animation metaphors in native and non-
native students’ writing. An account of the results in terms of the extent, lexico-
grammatical patterns, differences and similarities in the use and lexical content of
animation metaphors across the two corpora is provided.
1. Introduction
Theoretical work on metaphor led by Lakoff and Johnson (1980) and Lakoff (1987;
1993) has argued that metaphor is not just a “pleasing linguistic ornament” but a process
of human thought and reasoning. In their view, metaphor goes beyond artistic or
esthetic purposes since it helps to better understand certain abstract concepts.
Metaphorical expressions, as they point out, are used effortlessly in everyday life by
ordinary people to make manifest the concepts underlying our everyday understanding
of events and experiences. Lakoff and Johnson (1980) also suggest that since metaphors
result from our clearly delineated and concrete experiences, they play a key role in
helping us to construct and structure highly abstract and elaborate concepts.
The fact that most metaphorical linguistic expressions used as evidence to
support these theoretical claims come from elicited, constructed or idealized and
decontextualized cases, has resulted in a shift in focus in the metaphor research agenda.
With the development of discourse and corpus approaches to metaphor, the need to
apply cognitive metaphor theory to empirical discourse data has been highlighted (e.g.
Cameron and Low, 1999; Cameron, 2003; Charteris-Black, 2004; Deignan, 1999a,
1999b, 2005; Steen, 1999).
Applied linguistic approaches to metaphor connect “the conceptual with the
linguistic, in theory and in empirical work” (Cameron & Deignan 2006: 672) and
consider metaphor in its “natural environment of discourse” (Zanotto, Cameron &
Cavalcanti, 2008: 2). This renewed applied focus on metaphor research is also
concerned with methodological procedures for metaphor investigation and variation in
contextualized use of metaphor.
Drawing on naturally occurring empirical data from written discourse and taking
an applied linguistic approach to the study of metaphor, this exploratory corpus-driven
study aims to identify, describe and compare native and non-native students’ use of
linguistic metaphors in their argumentative written production, more specifically, their
use of animation and personification metaphors. The research questions guiding this
study ask firstly about the extent to which animation metaphors are used by native and
non-native students; secondly about the lexico-grammatical patterns of animation
metaphors in these specific contexts of use; and finally, about the differences and
similarities in the use and lexical content of animation metaphor in the two corpora
under analysis.
1
In J. Barden, M. Lee, J. Littlemore, R. Moon, G. Philip, and A. Wallington (Eds.), Corpus-based
Approaches to Figurative Language: A Corpus Linguistics 2009 Colloquium. Cognitive Science Research
Papers (pp. 39-48). Liverpool / Birmingham: University of Birmingham, United Kingdom.

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2. Animation and Personification
In their discussion of Conceptual Metaphor Theory (CMT), Lakoff and Johnson (1980)
assert that our experiences with our own bodies and with physical objects provide the
basis for a great variety of “ontological metaphors”. These allow us to view events,
activities, emotions and ideas, as objects, entities and substances. They argue that
personification is “perhaps the most obvious ontological metaphor” since the concrete
object is further specified as human. Personification, then, helps us understand
nonhuman phenomena in human terms, that is, on the basis of human characteristics,
goals, motivations, and actions.
With a focus on metaphor in poetry, Lakoff and Turner (1989) looked at
personifications of time (e.g. TIME IS A CHANGER, TIME IS A DESTROYER) and of
events such as death and life. They argued that we commonly conceive of agentless
events metaphorically making use of the general EVENTS ARE ACTIONS metaphor,
which “imputes agency to something casually connected to the event” (p. 37). They also
mention that the metaphorical agents frequently take human qualities since humans are
“readily identifiable as agents of specific kinds” (p. 38).
Although claims on the conventionality and abundance of personification in
literature and everyday discourse have been made (e.g. Lakoff and Johnson, 1980;
MacKay, 1986; Lakoff and Turner, 1989; Kövecses, 2002), there have been few but
very important attempts at examining personification in nonliterary contextualized data
(Goatly, 1997; Cameron, 2003; Charteris-Black, 2000, 2004; Charteris-Black &
Musolff, 2003; Dorst, 2008) and particularly in academic writing (e.g. Low, 1999, who
investigated the acceptability of the metaphor AN ESSAY IS A PERSON).
As previously stated, personification metaphor uses Vehicle terms from the
domain of people to refer to Topics that are nonhuman. However, as Cameron warns,
personification is one type of the wider category of animation, in which vehicle
domains are animate but not necessarily human (Cameron, 2003: 241). The use of
animate language, is a process by which “the abstract is made tangible and given
meaning through the use of conventional knowledge about the existence and behaviour
of living things” (Charteris-Black, 2000: 158-159). Animated Vehicle terms, thus,
belong to the domains of living things and living organisms such as plants and animals.
As living beings, plants, animals and humans share certain characteristics: a) they group
together in families, b) they interact with each other and their neighborhood and, c) they
have a circle of life: birth, growth, reproduction, and death. Thus, this paper explores
animation metaphor by focusing on Vehicle terms from the domains of PLANTS,
ANIMALS and HUMANS/PEOPLE, the later referred to as cases of personification
metaphors.
3. Corpus Data and Methodology
The data for this comparative exploratory study consists of two small corpora. First, the
native student sample comprises 17 argumentative essays extracted from the Louvain
Corpus of Native English Essays (LOCNESS). The texts were written by native-speaker
American students and total 10364 words. In order to compare native students’ use of
animation metaphor with non-native students’, a second sample was taken from The
International Corpus of Learner English (ICLE). This second sample is made up of 18
argumentative texts and it contains 10365 words. The non-native essays were written by

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students of English as a foreign language whose L1 is Spanish. For consistency reasons,
similar topics of the native and non-native students’ essays were selected (money, crime
and the prison system).
These two corpora are fully comparable: the native students’ (NS) essays were
written by 2nd or 3rd year university students and the non-native students’ (NNS)
essays were written by university students with upper-intermediate to advanced
proficiency levels. As shown in Table 1, the two sample corpora are also comparable in
terms of size; genre, text type and topics.
Table 1: The corpora used in this exploratory study
N of
TEXTS
N of
WORDS
GENRE
TEXT
TYPE
TOPICS
LOCNESS (NS) 17 10364 argumentative essay money / crime / prison
ICLE (NNS) 18 10365 argumentative essay money / crime / prison
TOTAL 35 20729
This study followed a corpus-driven methodology that, by having no pre-defined
categories to be searched for, allows new categories to emerge from the corpora
(Tognini-Bonelli, 2001) and is also free of “pre-tagged texts” (Sinclair, 2004: 191).
Through manual searching and manual annotation, all instances of linguistic
metaphor were first identified in the whole corpora under investigation. This was not a
straight forward process. From actually working with the data, it was realized that the
application of a combined procedure for metaphor identification was suitable in order to
assure systematicity and accuracy in the identification process. First, the Metaphor
Identification through Vehicle terms (MIV) procedure developed by Cameron (1999,
2003, 2006) was followed. With a prosaic view of linguistic metaphor, understood in
this study as a stretch of language that has the potential to be interpreted metaphorically
(Cameron, 2003; 2006), possible Vehicle terms (V-terms) that have the potential for
incongruity were identified. Then, to strengthen the accuracy of the identified V-terms,
the MIP procedure proposed by the Pragglejaz Group (2007) was applied.2
Afterwards,
all identified linguistic metaphors were further analyzed in their contexts of use to
identify and describe the linguistic form (of V-terms) and lexical content (Topics) of
animation and personification metaphors.
The quantitative side of the analysis consisted of frequency counts to show both
the extent to which linguistic and animation metaphors were used and their distribution
in the two sample corpora under study. Quantitative calculations were aided by the use
of the SPSS software which was also used to perform the quantitative analysis of
animation and personification metaphors as regards their lexico-grammatical form and
distributional patterns across the two corpora.
2
For a more comprehensive account of the methodological procedures for metaphor identification
including information about the treatment of closely related linguistic phenomena (e.g. metonymy) and
fuzzy cases, see Chapetón and Verdaguer (forthcoming).

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4. Results and Discussion
4.1 Gauging the presence of animation metaphor
Frequency counts of the presence of linguistic metaphor and animation metaphors are
given in Table 2. The linguistic metaphors identified in the whole dataset, which
account for a total of 2929 items (14.13%), are similarly distributed across the two sub-
corpora. The NS’s corpus contains 1467 instances of linguistic metaphor (14.15%)
from which 200 instances (1.93%) were identified as realizing animation metaphors. In
the NNS corpus the raw frequencies and percentages were similar with 1462
occurrences of linguistic metaphor (14.11%) from which 191 instances were identified
as animation metaphors (1.84%). The total number of animacy expressions (391) is
similarly distributed between NS and NNS accounting for 51.2% (200 items) and 48.8%
(191 items) respectively.
Table 2. Instances of Linguistic Metaphors and Animation Metaphors in Native (NS) and
Non-Native Students’(NNS) Samples of Argumentative Writing.
N of
Essays
N of
Words
Linguistic Metaphors Animation Metaphors
Freq. % Freq. %
NS 17 10364 1467 14.15 200 1.93
NNS 18 10365 1462 14.11 191 1.84
TOTAL 35 20729 2929 14.13 391 1.89
The lexico-grammatical distribution of animation metaphor is shown in Table 3. The
quantitative evidence shows that animation metaphor with verbs as Vehicle terms are
the most common form in the two sub-corpora under investigation. On the whole, these
are followed by multi-word lexical verbs3
. Noun phrases and single nouns rank third
and fourth respectively. Yet, the NNS sub-corpus shows a higher frequency of animated
single nouns.
Table 3. Lexico-grammatical forms of Animation Metaphor in Native (NS) and Non-Native
Students’ (NNS) Samples of Argumentative Writing.
Word class NS NNS WHOLE
Freq % Freq % Freq %
Noun 4 2.0 19 9.9 23 5.88
Noun Phrase 13 6.5 12 6.3 25 6.39
Verb 149 74.5 135 70.7 284 72.63
Multi-word lexical verb 18 9.0 15 7.9 33 8.44
Verb Phrase 7 3.5 5 2.6 12 3.07
Adjective 7 3.5 3 1.6 10 2.56
Adverb 1 0.5 1 0.5 2 0.51
Preposition 1 0.5 0 0.0 1 0.26
Prepositional Phrase 0 0.0 1 0.5 1 0.26
TOTAL 200 100 191 100 391 100
3
Following Biber et al (1999:403), multi-word lexical verbs in this study include phrasal verbs,
prepositional verbs and phrasal-prepositional verbs. However, other multi-word verb constructions such
as verb + noun phrase were categorized as Verb Phrases.

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If presented in group forms, that is, merging single and phrase-level categories, verbal
animation metaphors stand out accounting for 84.14% of the total. Animation
metaphors taking the nominal form represent 12.27% of the data while the remaining
3.59% take adjectival, adverbial or prepositional forms.
Cognitive theories of metaphor have not attempted to be predictive of the lexico-
grammatical forms of metaphor (Deignan, 1999b). As far as animation metaphor is
concerned, the evidence shown to exemplify personification metaphor (though most of
the examples used in Lakoff and Johnson, 1980: 33 were reused in Kövecses, 2002: 35)
is restricted to verbs. However, the fact that animation metaphor in this empirical study
takes also nominal, and to a less extent, adjectival, adverbial and prepositional forms,
may suggest that animation, though mainly realized by, is not limited to, verbal forms.
Examples of the linguistic variety of animation metaphors will be shown in the next
section.
4.2 Exploring the lexical content of animation metaphor
A qualitative analysis of the data shows that a variety of inanimate entities are treated as
humans or living organisms. By looking closely at instances of animation metaphor in
this specific context of language in use, it was found that most of the Topics of
animation metaphors were abstract ideas or abstract entities such as actions or activities
(e.g. business and crime), facts / situations, systems and money. Concrete or physical
entities such as things or books that were given human intentional action were present to
a lesser extent. As shown in Figure 1, within the range of Topics referred to as animate
by both native and non-native students, actions, facts / situations, evil, crime (more
recurrent in the NS corpus) and money (more recurrent in the NNS corpus) stand out
with the highest frequencies, while Topics such as time, illness, history or language
were less frequent.
Figure 1. Common Topic Domains of Animation Metaphor in NS and NNS Writing.
Actions or activities were personified as shown in the following examples: government
relief, the restoration of the family, actions (e.g. of possessing and buying) are seen as
agents endowed with intentional human capacities:
(1) Too much government relief, over the years, has taken away people's pride (NS’s)4
4
Examples are shown as they were actually written by the native and non-native students. Texts were
only checked for typos. Single underlining is used to mark instances of animation and personification
metaphor.
0
10
20
30
40
50
60
70
80
Actions
/
Activities
Business
Crime
Situation
/
Fact
Evil
Money
Systems
Book
/
Essay
Ideas
Theory
/
research
Time
Illnes
Law
History
Feeling
/
Emotion
Language
Things
Others
NS
NNS

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(2) …the restoration of the "American Family",[…] , must take the front seat… (NS’s)
(3) …get engaged in actions which endanger other people's basic rights… (NNS’s)
(4) …to posses one has to buy and that requires money. (NNS’s)
The activity of buying and selling, generally known as business, was animated by being
given life (5) and personified as being able to succeed (6) or feel (7). It was also seen as
an adversary or enemy able to perform a violent action (8):
(5) Since that the trade was born (NNS’s)
(6) …crime pays for an industry that thrives on it. (NS’s)
(7) This is what irritates business… (NS’s)
(8) If we, the people, allow big business to pollute and rape our land,… (NS’s)
Crime, defined as “an illegal activity or action” (Rundell and Fox, 2007), was also
personified, on the one hand, as a destroyer (9) and on the other, it was atrributed
positive human characteristics such as being able to succeed (10), or as a handsome
leader as shown in examples (11) and (12):
(9) …crime does not pay and… it destroys your life. (NS’s)
(10) A similar [criminal] case succeeded in Spain (NNS’s)
(11) Lying, fraud, deception, and theft sometimes lead to greater profit (NS’s)
(12) Organized crime, on the other hand, pays off quite handsomely. (NS’s)
Circumstances, situations and facts were talked about as sad or cold (e.g sad reality) and
also attributed human agency or animate characteristics:
(13) …the fact of going to prison does not refrain people from making a crime. (NNS’s)
(14) This situation produces… [greedy people] (NNS’s)
(15) Some circumstances make him to commit assassination. (NNS’s)
(16)…events which reside in the darker portions of this country's history… (NS’s)
Problems or problematic situations or circumstances were also animated by the
attribution of physical or body related actions:
(17) …they have no idea where the center of the problem lies. (NS’s)
(18) There are many problems, but three of them stand out. (NNS’s)
It was common to find, both in the native and in the non-native students’ corpora, cases
in which circumstances, situations or facts were first described and then referred to as
agents of a human action:
(19) Goodrich Company did not mind that they were going to lose money on the original deal,
but they knew that aircraft brakes would end up to be very profitable for them. This just
shows that profit will come in the long run… (NS’s)
Other examples of this kind from the NS include: This (fact, situation) states / hurts /
explains / helps to explain / supports etc., and from the NNS: This (fact, situation)
appeared /contradicts / came up / means / makes (e.g. something difficult) / needs
/shows.
Animation metaphors for Evil and Money were recurrent in both corpora. The high
frequency may be explained by the fact that, as previously mentioned, money is one of
the essay topics of the corpora under study (See section 3). More precisely, the
statement “In the words of the old song: Money is the root of all evil” was the prompt
given as one of the suggested essay titles (Granger, Dagneaux & Meunier, 2002). Both,
NS and NNS reused the animation metaphor “the root of all evil” in 12 and 7 occasions

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respectively. In the case of the NS’s corpus, these instances account for 100% of the
animation metaphors for Evil, while in the NNS’s corpus, Evil was further personified
as an actor or character in a play:
(20) It is precisely at this point that evil is likely to make its appearance onto the stage…
(NNS’s)
(21) …the evil waits his moment for acting. (NNS’s)
Evil was also personified as having a friendly relationship with Money:
(22) evil had other friend [money] and together began their gait by the world. (NNS’s)
Similarly, personified relationships between Evil and Money were found in the NS’s
sample corpus:
(23) …if indeed money inspired and cultivated evil…
(24) … the problem is how these two desires are to be reconciled, called "compatible"…
Money, in the NS’s corpus under analysis was personified as somebody who can(not)
buy, (25), provide opportunities (26), make mistakes (27), or decisions (28), or as an
animated being able to perform physical action and movement (29):
(25) the things money can buy // Money cannot buy you friends, health of[or] happiness, …
(26) Money can provide opportunities to educated the uneducated…
(27)…[history makes] money such a fallible - measuring stick of […] evil.
(28) Money is incapable of being evil […] and cannot make judgments and decisions.
(29) Tax deductions will run about $69.00 per week
Money was seen in the NNS’s corpus as a (male) person or a living being (30), as an
actor of a film or play since it disguises and plays roles (31), as a provider of help (32)
or as somebody with power and able to influence or rule people’s lives (as a governor or
as controller) (32):
(30) a. friends with Mr. Money //we will see the positive face of money // the prestige of money
b. money will survive and never die. // …the smell of money
(31) a. economy disguised beneath the appearance of friendship
b. the role money play in the world // money plays a very important role
(32) Money can help to mitigate many disabilities, calamities and much suffering
(33) a. the allure of money compels human beings
b. money has influenced pleople behavior […] since its very birth.
c. there are slaves of money // we are under its control, // The vital needs subordinated to it,
Less frequent Topics referred to as animate include, for instance, personifications of
Literary plays and Books. These were found in NS’s (4 instances) and NNS’s (6
instances) texts with verbs of human action such as stating and telling. Similarly,
instances of “This essay/paper shows/states” were found (at least once) in each sample
corpora. Interesting expressions for further systematic analysis (e.g. of their
acceptability) found in the NNS’s corpus include: “this essay wants to mean // it is
trying to mean”.
Few personifications of Time, a common example in cognitive metaphor theory,
were found in the NS’s (2 instances) and in the NNS’s (1 instance) samples. It was
personified as passing and going by. A single reference to Theory was found in the
NS’s sample as working, while references to Studies or Research were found in both
corpora as showing (e.g. “Studies have shown”). Another example of personification in

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Lakoff and Johnson (1980) is religion. It was found only once in the NNS’s sample:
“religion did not allow him to punish criminals”. Reference to illness/injury was also found
twice in the NS’s sample (coming about, keeping someone from) and once in the NNS’s sample
(finishing with lives).
5. Final Comments
Animation and personification metaphor has been widely used as a literary device
particularly in poetry and in children’s literature (Knowles and Moon, 2006). This paper
represents an attempt to explore these types of metaphor in nonliterary corpus data.
Similar distributional results were obtained when quantifying the instances of linguistic
metaphor and more specifically when examining the extent to which animation was
used in this sample corpus of native and non-native students’ writing. An analysis of
the lexico-grammatical form reveals a prevailing verbal-based pattern of the linguistic
realizations of animation metaphor in both NS’s and NNS’s corpora; a finding that goes
in line with Cameron’s (2003: 243).
When exploring the lexical content, that is the Topics of animation and
personification metaphor, it was noticeable that, as Goatly (1997) suggests, most were
abstract ideas. The examples discussed in 4.2 illustrate some of the most recurrent
Topics and the similarities and differences in animated or personified aspects that
students used in their production of animation metaphors.
The fact that animation and personification metaphors for Crime, Money and
Evil were recurrent in the corpora under analysis suggests, as Deignan (2008) asserts,
that the topic of a text is one of the factors affecting metaphor choice. By looking at the
linguistic realizations of animation metaphor, it was noticeable that the essay prompts
given to students play an important role in the “production” or re-use of animation
metaphor. Most of the linguistic realizations shared by NS and NNS include references
to Crime as paying (Essay prompt: “Crime doesn’t pay”) or “the root” of Evil (as
discussed in section 4.2).
As shown in Figure 1, most of the lexical content (Topics) of animation and
personification metaphor examined in this exploratory study was common to both
sample corpora. Both NS and NNS personified abstract entities such as Activities (e.g.
business and crime), circumstances, situations or facts and also more concrete entities
such as Books. However, differences lie mainly at the heart of their linguistic
realization. The variety of Vehicle terms or metaphoric expressions used to animate or
personify those entities is illustrated by the several examples shown in section 4.2.
Besides, differences between the introspective, elicited examples provided by cognitive
theories and the attested ones support the need for further attempts at investigating
linguistic and conceptual metaphor in naturally occurring contextualized data.
In general, the attribution of human agency to abstract events and, to a lesser
extent, to concrete entities, was found to be more frequent, in the data under analysis,
than the attribution of animated characteristics from the source domains of PLANTS and
ANIMALS. This may support Lakoff’s and Johnson’s (1980) claims about ontological
metaphors and personification (see section 2) as well as Lakoff’s and Turner’s (1989)
EVENTS ARE ACTIONS general metaphor. However, a bigger corpus and further
analysis would be desirable before any generalizations could be made.

9. 47
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