RECESSION:
Defining Source Domains through WordNet and SUMO
*Siaw-Fong Chung*Kathleen Ahrens**Chu-Ren Huang
National Taiwan University* Academia Sinica**
Graduate Institute of Linguistics, National Taiwan University, No. 1, Section 4,
Roosevelt Road, Taipei, Taiwan R.O.C. 106*No.128, Sec. 2, Academia Road, Nangang, Taipei, Taiwan R.O.C. 115**
claricefong6376@hotmail.com, kathleenahrens@yahoo.com, churen@gate.sinica.edu.tw

Abstract
This paper argues that the sorting of metaphorical expressions according to source
domains can be verified through using the WordNet lexical knowledgebase and SUMO
ontology. This study uses the examples from RECESSION and demonstrates that
expressions such as ‘painful recession’ and ‘the exacerbated recovery’ are related to thedecreasing physical state of organism (i.e., ECONOMY IS A DISEASE). The
relatedness of these expressions can be established using WordNet and SUMO fromwhich their related senses and ontological nodes can be identified. This study
strengthens the metaphorical analyses that are carried out intuitively. This researchcontributes to the study of language processing and more specifically the processing ofconceptual metaphors at the cognitive level.

1.0 Introduction
In an English corpus-analysis based on the 1995 Washington and Los Angeles Post (available
through the Language Data Consortium), metaphorical expressions related to the target domain
RECESSION were examined. These metaphorical expressions were analyzed based on theConceptual Mapping (CM) Model (Ahrens 2002, Ahrens, Chung and Huang 2003, Chung, Ahrens
and Huang, 2003a and Chung, Huang and Ahrens 2003b). In this model, a conceptual metaphor is
identified when there is a mapping between the concrete source domain and the abstract target
domain. Using this model, examples discussed previously are IDEA IS BUILDING and
ECONOMY IS A DISEASE. As a model based on the Contemporary Theory of Metaphor (CTM)(Lakoff and Johnson, 1980), the CM model extends beyond the CTM by providing the specific
Mapping Principle (MP) between the source-target domain mapping. This Mapping Principle statesin specific terms which aspect of the source and target domains is mapped in conceptual metaphors.
In this paper, the discussion of RECESSION will extend from the previous work on corpora
analyses of conceptual metaphors. This study aims to define the source domains related to
RECESSION through examining linguistic expressions occurring within the each source domain.
Expressions such as ‘recover from recession,’ ‘plagued by recession’ and ‘painful recession’ will be
examined using WordNet and an upper ontology (Suggested Upper Merged Ontology or SUMO) todefine the relatedness of these expressions with their respective source domain. The incorporationof computational tool reduces the manual determination when sorting out the expressions according
to source domains. Chung, Ahrens and Huang (2004) examined the target domain of CAREER and
CULTURE. One aim of this paper is to verify the steps operationalized in Chung et al. (2004) byinvestigating another target domain --- RECESSION. The steps involved are: First, extract all
metaphorical expressions from the corpora. Second, select the most concrete sense from WordNet(through the Academia Sinica Bilingual Ontological WordNet (BOW; available at
http://bow.sinica.edu.tw). The WordNet senses lead to the related ontological nodes of SUMO.
Third, examine the Wordnet explanation then later the SUMO nodes to decide which source domain

these expressions belong to. For instance, in Chung et al. (2004), they found that the phrase shiye
youhuan yishi 事 業 憂 患 意 識 ‘the worried consciousness of career’ is mapped onto theconsciousness of PERSON. Through these steps, the linguistic items were sorted into different
source domains. In the following section, this paper first outlines the theoretical framework of theanalysis.

2.0 Conceptual Mapping (CM) ModelIn the CM Model (Ahrens 2002), a conceptual metaphor is identified when a concrete source
domain is mapped onto the abstract domain such as in IDEA IS BUILDING in the example (1). The
abstract notion is placed in the square box and the underlined expression is the concrete concept of
the BUILDING.
(1) nide lun dian gen ji shi sheme
your argument base be what
‘What is the foundation of your argument?’
Based on a group of conventional examples, the CM Model proposes a Mapping Principle (MP)for each conceptual metaphor. For instance, the MP for IDEA IS BUILDING is “Idea is understoodas building because buildings involve a (physical) structure and ideas involve an (abstract)
structure (Ahrens 2002:279),” in which the ‘structure’ of the building is found consistently mappedin most of the metaphorical expressions.
Using this model Ahrens et al. (2003) and Chung et al. (2003a and 2003b) examined corpora
examples of ‘economy’ in Chinese and English. In these studies, they proposed that the mostprototypical (most frequent) mapping between the source and target domains helped underline theMPs. Since domain knowledge mappings involve structures, they adopt ontology to represent andverify knowledge structure. Ahrens et al. (2004) also used SUMO and WordNet to verify the MPs in
the studies. This study will incorporate WordNet in addition to the CM Model and corpus analysis.

3.0 WordNet and SUMOIn the proposal of Niles and Pease (2003), the lexicons in WordNet and the SUMO nodes werelinked and the semantic definitions to the SUMO nodes were provided. In SUMO, nodes arearranged hierarchically to represent human knowledge representation. Huang, Chang and Lee (2004)
further developed the WordNet and SUMO system by providing a Chinese-English bilingual versionof the system (BOW or Academia Sinica Bilingual Ontological WordNet (available at
http://bow.sinica.edu.tw). This paper makes use of BOW in the analysis of conceptual metaphors.

4.0 Methodology
There were two main steps in the analysis of the metaphorical instances of RECESSION in thispaper. The first step involved identifying the metaphorical expressions in the corpus. The second
step involved verification of these linguistic expressions through WordNet and SUMO.
In the first step, a search based on the target term RECESSION was carried out in the 1995
Washington and Los Angeles Post through the Language Data Consortium (available at
http://www.ldc.upenn.edu/ldc/online/index.html). The first 400 instances were extracted from thesearch and these instances were examined manually to determine the source domains within which aconceptual metaphor occurred. All the metaphorical expressions and their tokens are shown in Table
1.
Within the total 400 instances of ‘recession,’ 119 conceptual metaphors were found. As in
Chung et al. (2004), the items were not sorted according to source domains at this stage. This is to
avoid grouping the items manually to their respective source domains.

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Table 1 : Metaphorical expressions related to RECESSION
Conceptual
Conceptual
TokensTokensMetaphors
Metaphors
burst out of1
slide into1

Conceptual
Metaphors
severeclimbing out of1

slip back1

recovery

4

deep

6

slip into

7

suffer4

deepening

1

throw into2

recover3

deepest2

thrust into1

painful2drag into1

tip into

2

exacerbate

1

drive back into1

tip over1

plague1

drop into2

tumbling into

1

derailed

1

emerging from1

buffeted

1transitory

1

enter

2

hit

4

(drive) up

1

fall into2

bore the brunt

1

send

1

go down into

1

battered by

1

fuel

1

go into4

protected

1

crushed by

1

in

3

protections

2

headed for2

lapse back into1

struggling

1

enmeshed1

mire in2

cushion1

fueled by2

out of3

battling

1

to forestall1

pitch back into1

fending off

1

put into3

push into

5

plunged…into

7

protracted

1stung by1

bit in1keep out of

2

nasty

1

healthy1

growing1

Tokens5

In the second step, all the linguistic expressions related to RECESSION were searched for in
WordNet and SUMO using BOW. For each search, the most concrete sense of the linguistic item
was selected. Each sense will lead to the related SUMO node and an examination of these nodes
helps determine whether the list of words belong to the same source domain or not. An example of
selected expressions (shaded) from Table 1 is shown in Table 2.
The highlighted expressions are the key phrases that help to determine to which source
domains the expressions belong to. For instance, ‘fuel’ belongs to TRANSPORTATION and its
process involves ‘Putting’ something into the transportation. ‘Buffeted’ and ‘battling’ are related to
FIGHTING and their corresponding ontological nodes are ‘Motion’ and ‘Impacting’ (on the person
being hit). ‘Plunged..into’ belongs to WATER and this is further proven with its ontological node of‘Wetting.’ Lastly, ‘derailed’ belongs to TRANSPORTATION as well and the related ontologicalnode is ‘Motion.’
Through this step, the expressions are sorted into their respective categories and this reduces
the manual determination in the categorization process.

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Table 2: WordNet Senses and SUMO Nodes Related to RECESSION
Expressions WordNet
Explanation of Meanings from
Sense
WordNet
5: obtain fuel* provide with fuel; as of aircraft, ships,
fueland cars
buffeted
1: strikestrike, beat repeatedly
battling
2: strikestrike violently and repeatedly
plunged…
5: soak
immerse into a liquid
intoderailed
1: off trackof trains

Related
SUMO
Nodes
Putting(放置)Motion(移動)Impacting(影響)Wetting(濕潤)Motion(移動)[*Since BOW is a bilingual database, all English expressions that were searched in BOW will be provided withsenses in Chinese. This paper translates the Chinese senses into English.]In this following section, this paper first displays the sorting of items within the source
domain of DISEASE based on manual analysis, and also points out the problems with manual
sorting of source domains.

5.0 Manual Sorting of the Source Domains
The sorting of items in Table 1 is shown in Table 3 (see the next page). Focusing specifically on
DISEASE, one finds that some expressions are ambiguous. For instance, ‘healthy,’ ‘discovery,’‘suffer,’ ‘recover’ and ‘painful’ can both apply to PERSON as well as DISEASE. A healthy patient
can refer to both PERSON and DISEASE. Given this problems with the manual analysis, this paper
proposes to use WordNet and SUMO to find out the relationships between related source domains(such as that between PERSON and DISEASE). This can be done through examining the WordNet
and SUMO nodes, as demonstrated in Table 2.
Table 3 : Manually Sorted Metaphorical expressions According to Source Domains ION
Source Domains
Metaphorical Expressions Related to RECESSION
HOLE
burst out of, climbing out of, deep, deepening, deepest, drag into, drive
back, into, drop into, emerging from, enter, fall into, go down into, go
into, in, lapse back into, mire in, out of, pitch back into, push into, slide
into, slip back, slip into, throw into, thrust into, tip into, tip over, tumbling
into, put into, keep out ofWAR
buffeted, hit, bore the brunt, battered by, protected, protections, struggling,cushion, battling, fending off, to forestall
TRANSPORTATION derailed, transitory, (drive) up, send, fuel, crushed by, headed for,enmeshed
fueled by
PERSONnasty, growing
DISEASE
protracted, severe, exacerbate, plague
PERSON/
recovery, healthy, suffer, recover, painful
DISEASE (?)
WATER
plunged…into
ANIMALstung by, bit in
The next section shows the results of analyses using BOW. The results of the finally sorted list
for RECESSION IS DISEASE are shown first and the discussion will explain how the decision wasmade during the sorting process using BOW. This paper argues that the use of BOW helps to reduce46

the ambiguity encountered by manual analyses.

6.0 Defining the Source Domains Using WordNet and SUMO
The results of the finally sorted list for RECESSION IS DISEASE will be discussed in the next
sub-section, followed by RECESSION IS A PERSON.

6.1 RECESSION IS A DISEASEIn Table 4, all the expressions related to the conceptual metaphor RECESSION IS A DISEASE arelisted.
Table 4: Expressions related to RECESSION IS DISEASE (See Appendix for examples)
MetaphorsFrequency
MetaphorsFrequency
severe5painful2
recovery
4exacerbate
1
suffer4
plague1
recover3
healthy1
Most of the expressions in Table 4 occur more than once in the corpus searched. Their
WordNet and SUMO definitions in BOW were shown in Table 5.
Table 5: WordNet Senses and SUMO Nodes Related to RECESSION IS DISEASE
Expressions WordNet
Explanationof
Meanings
from Related
SUMO
Sense
WordNetNodes
1: dangerous causing fear or anxiety by threatening
SubjectiveAssessment
severegreat harm
Attributegradual healing (through rest) after
3: recovery
PhysiologicProcess
recoverysickness or injury
suffer1: painfeel pain or be in pain
PhysiologicProcess
recover
1: recover
get over an illness or shock
DiseaseOrSyndrome
painful
1: painful
causing physical discomfort
PathologicProcess
make worse
Decreasing
1:
exacerbate
deterioratea serious (sometimes fatal) infection
3: plaguetransmitted by the bite of an infected rat
DiseaseOrSyndrome
plaguedflea (especially bubonic plague)
having or indicating good health in body
2: healthy
healthy
DiseaseOrSyndromeor mind; free from infirmity or disease
From Table 5, all the related SUMO nodes are listed again in (2) below, with regrouping
under their upper nodes.
2. (a) Biological Attribute/Processes
PhysiologicProcess
DiseaseOrSyndrome
PathologicProcess(b) Physical Quantity
Decreasing(c) Nominative Attribute
SubjectiveAssessmentAttribute

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From (2) above, most of the expressions in Table 5 refer to a physical state that is becoming
worse or sick. The only exceptional case is ‘severe,’ which is categorized under‘SubjectiveAsessmentAttribute.’ The problematic category of ‘severe’ suggested another possible
source domain for these expressions, i.e., PERSON. In fact, PERSON and DISEASE are closely
related as ‘physical discomfort’ or ‘harm’ and ‘fear’ are part of the feeling of a person whensomeone is unwell. In order to find out whether DISEASE is subsumed under a general source
domain of PERSON, this paper examines the definitions of the ontological nodes in (2), as shown in
Table 6.
Table 6: Source Domain of DISEASE: Related SUMO Nodes and Their Definitions
SUMO Nodes
Definitions
PhysiologicProcess
A normal process of an Organism or part of an Organism.
DiseaseOrSyndrome
A BiologicalAttribute which qualifies something that alters or interfereswith a normal process, state or activity of an Organism. It is usuallycharacterized by the abnormal functioning of one or more of the host's
systems, parts, or Organs.
PathologicProcess
A disordered process, activity, or state of the Organism as a whole, of abody system or systems, or of multiple Organs or Tissues. Included here
are normal responses to a negative stimulus as well as patholologic
conditions or states that are less specific than a disease. Pathologic
functions frequently have systemic effects.
Decreasing
Any QuantityChange where the PhysicalQuantity is decreased.
SubjectiveAssessment
The Class of NormativeAttributes which lack an objective criterion for
Attributetheir attribution, i.e. the attribution of these Attributes varies from subject
to subject and even with respect to the same subject over time. This Classis, generally speaking, only used when mapping external knowledge
sources to the SUMO. If a term from such a knowledge source seems tolack objective criteria for its attribution, it is assigned to this Class.
Indeed, we find that most of the expressions in Table 4 are related to the ‘BiologicalAttribute’or ‘PhysicalQuantity’ of the ‘Organism.’ In addition, this ‘Organism’ is one that is with the state ofmind, i.e., one who has ‘SubjectiveAssessmentAttribute’ and can make subjective judgment. In ourprevious work on ECONOMY IS A PERSON (Ahrens et al. 2003, Chung et al. 2003a), there arenodes relating to DISEASE but they are subsumed under PERSON. This can be seen in Table 7 (seethe next page), in which the expressions related to DISEASE (symptom, dysfunctional, deteriorate
and recover) are ambiguous expressions that can relate to DISEASE or PERSON. In the previous
work, they were not separated from the source domain of PERSON. There could be one reason
behind this decision, i.e., the percentages of occurrences for these few items are low. Therefore, they
do not form a separate source domain by themselves.
Upon discovering the relatedness of PERSON and DISEASE, the next step is to find out
other expressions that belong to the source domain of PERSON, in addition to those in Table 4. The
following section will deal with this part.

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Table 7: ECONOMY IS A PERSON: Definitions from WordNet and SUMO
WordNet%
Items
Explanations
SUMO Nodes
Senseschengzhang
the process of an individual organism
67(growth)growing organically; a purely biological (55%)
6: growth
unfolding of events involved in an
Growth(生長)organism changing gradually from a
simple to a more complex levelshuaitui
any weakening or degeneration
8 (7%) PathologicProcess
4: atrophy(dysfunction)(especially through lack of use)
(病理歷程)
chengzhangchi
2(2%)
x(growth period)
bingzhuang
an impairment of health or a condition of 1(1%) DiseaseOrSyndrome
2: disease(symptoms)
abnormal functioning(疾病或症候群)
mingmai
2 (2%)
1: lifeblood the blood considered as the seat of vitality
Blood(血液)(lifeblood)shuaitui
1 (1%)
1:(weakness anda symptom of reduced quality or strength
Damaging(損害)deterioration
degeneration)
chengzhang
21
2: agegrow old or older
Growth(生長)(grow)(17%)
shuaitui
5 (4%)
DiseaseOrSyndrome(to become
1: atrophy undergo atrophy(疾病或症候群)
dysfunctional)
fusu
9 (7%)
4:
OrganismProcess(regaina revival of consciousness
resuscitation(生命歷程)
consciousness)ehua
1:
4 (3%) DiseaseOrSyndrome
grow worse(deteriorate)
deteriorate(疾病或症候群)
1 (1%) DiseaseOrSyndrome
huifu
2:
get over an illness or shock(疾病或症候群)(recover)recuperate
Total
121
(100%)
Note: ‘x’ indicates that the related WordNet sense was not found in the SinicaBOW.

6.2 RECESSION IS A PERSONIn addition to the expressions in Table 3, two more expressions are related to PERSON. These
expressions are shown in Table 8.
Table 8: Expressions related to RECESSION IS DISEASE (See the Appendix for examples)
RECESSION IS DISEASE
Frequency
grow

1

nasty

1
The search in Wordnet and SUMO shows the following results in Table 9.

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Table 9: WordNet Senses and SUMO Nodes Related to RECESSION IS DISEASE
Expressions
WordNetExplanationof
Meanings
from Related
SUMO
Sense
WordNet
Nodes
Sense 4increase in size by natural process; ofGrowthgrow
living matter, such as plants and animals
Sense 2
characterized by obscenity
SubjectiveAssessment
nasty
Attribute
In Table 9, one finds that ‘grow’ is related to the ‘living matter’ and the SUMO node of‘Growth,’ which is defined as ‘The Process of biological development in which an Organism or partof an Organism changes its form or its size.’ Therefore, this expression is categorized under
PERSON. For ‘nasty,’ the same ontological node is found with ‘severe,’ namely‘SubjectiveAssessmentAttribute.’ As discussed earlier, this node reflects the ‘state of mind,’ andtherefore is also related to PERSON. To produce a more complete picture, the relationship between
PERSON and DISEASE as well as ‘Organism’ can be shown in Figure 2.
Figure 2: Representations of the Metaphorical Instances According to Source and Target
Domains and their Relationship with SUMO

Target:CULTURE
self-examination
life
productivity
temparament
growthwalk

Source:PERSON
TraitAttribute(人格特質)
Living(活的)BiologicalAttribute(生物屬性)PsychologicalAttribute(心理屬性)
Growth(生長)liveliness
hurt
Target:CAREER

maps onto

Walking(行走)Organism(生物體)EmotionalState(情緒狀態)
SubjectiveAssessment Attribute
Source: DISEASE
DiseaseOrSyndrome
PhysiologicProcess
PathologicProcess
Decreasing

nasty
grow severe
plagued healthy
recoverrecoverysufferpainfulexacerbate
Target:
RECESSION

SUMO: Organism

50

In this figure, DISEASE is subsumed under PERSON and both of these source domains have thecorresponding node of ‘Organism.’ Since in this section, we have not included the examples related
to RECESSION IS A PERSON. The following section will provide the other expressions from thesource domain of PERSON.

7.0 Relatedness of DISEASE and PERSON
Ahrens et al. (2003) pointed out that WAR is related to COMPETITION by sharing the concept of‘ViolentContest’ in SUMO. In this paper, we found that DISEASE is related to PERSON by sharing
the concept of ‘Growth’ and ‘SubjectiveAssessmentAttribute.’ These discoveries about theoverlapping of concepts are crucial in understanding human cognition.

8.0 Conclusion
From the analysis of the metaphorical expressions related to RECESSION, this paper found theinterrelatedness of human concepts in expressing metaphors. The relationship is complex andthrough linguistic analysis, the model of human conceptualization becomes more understandable.
The future aim of this study is to link all the source and target domains with their related concepts.
Work in this direction will lead to systematizing the extraction of conceptual metaphors in the formof a lexical resource such as WordNet.
To summarize, the analysis in this paper, metaphorical expressions are found reflecting themetaphors RECESSION IS A DISEASE and RECESSION IS A PERSON. The sorting of thelinguistic expressions into their respective source domains can be carried out through two main
steps. First is the extracting of metaphors through corpora and second is the sorting out of source
domains through WordNet and SUMO. In addition to these, the most important finding in this paper
is the discovery of the relatedness between PERSON and DISEASE. This discovery can be referredback to our previous work in examining the metaphor ECONOMY IS A PERSON, in which there
are nodes relating to DISEASE but they are subsumed under PERSON. Through this paper, we findthe reason why both of these concepts are related, i.e., they are both referring to the physicalcondition of the ‘Organism’ and this ‘Organism’ is not any ordinary ‘Organism’ but that that has thestate of mind with ‘SubjectiveAssessmentAttribute.’ Therefore, the source domain of PERSON issuggested rather than any other forms of living things.
To conclude, this study refines the examination of the conceptual metaphors using the CM
Model by verifying the source domains using WordNet and SUMO. This study proposes a way toprove that a list of linguistic expressions can be found within a single source domain throughobserving the similar ontological nodes where these expressions occur. Such method provides
linguistic evidence to identify specifically the knowledge representation when a conceptual
metaphor is expressed. This study has implications on understanding language processing in
cognitive science through using linguistic corpora.
For future work, we would like to incorporate psycholinguistic experiments to verify themetaphorical expressions that have been extracted through corpora. If the metaphorical expressions
are found by the subjects to be related to the source and target domains chosen, this will help
verifying the workability of the CM Model in predicting the occurrences of metaphors in corpora as
well as textual analyses.

Acknowledgments
We would like to thank the LSK reviewers for their comments on this paper and NSC grants for supporting the
research discussed in this paper. Any remaining errors are the sole responsibility of the authors.

51

Appendix
RECESSION IS DISEASEl) Mexico had entered a severe RECESSIONa) the budget in seven years might cause or exacerbate am) the peso crisis results in a severe RECESSION

RECESSIONn) as a result of the severe 1981-82 RECESSIONb) This isn't because a painful RECESSIOo) Mexico also suffered a RECESSIONc) the region 's long and painful RECESSIONp) Mexico also suffered a RECESSIONd) His government has been plagued by an economicq) and the nation suffered RECESSION

RECESSIONr) One reason is a stronger economy and the recoverye) many industries within it have recovered nicely from thefrom the RECESSION

RECESSIONs) The Japanese economy is suffering from a protractedf) airline traffic began to recover from the RECESSION

RECESSIONg) Japan 's recovery from a long and harsh RECESSIONr) plunged the economy into a severe RECESSIONh) recovery after four years of RECESSION

RECESSION IS A PERSONi) The world 's recovery from the global RECESSIONa) a growing RECESSIONj) since the economy was recovering from a RECESSIONb) A healthy RECESSIONk) economists said that a severe RECESSIONc) The economy could by then be in a nasty
RECESSION

References
Ahrens, Kathleen. 2002. “When Love is Not Digested: Underlying Reasons for Source to Target Domain
Pairings in the Contemporary Theory of Metaphor.” In Yuchau E. Hsiao (ed.). Proceedings of the First
Cognitive Linguistics Conference. Cheng-Chi University, pp. 273-302.
Ahrens, Kathleen, Siaw-Fong Chung and Chu-Ren Huang. 2004. “From Lexical Semantics to Conceptual
Metaphors: Mapping Principle Verification with WordNet and SUMO.” In the Proceedings of the 5th
Chinese Lexical Semantics Workshop (CLSW-5), Singapore. pp. 99-106.
Ahrens, Kathleen, Siaw-Fong Chung and Chu-Ren Huang. 2003. “Conceptual Metaphors: Ontology-based
Representation and Corpora Driven Mapping Principles.” In the Proceedings of the ACL Workshop on the
Lexicon and Figurative Language. pp. 35-41.
Chung, Siaw-Fong, Kathleen Ahrens and Chu-Ren Huang. 2004. “Using WordNet and SUMO to Determine
Source Domains of Conceptual Metaphors.” In the Proceedings of the 5th Chinese Lexical Semantics
Workshop (CLSW-5), Singapore. pp. 91-98.
Chung, Siaw Fong, Kathleen Ahrens and Chu-Ren Huang. 2003a. “ECONOMY IS A PERSON: A
Chinese-English Corpora and Ontological-based Comparison Using the Conceptual Mapping Model.” In
the Proceedings of the 15th ROCLING Conference for the Association for Computational Linguistics andChinese Language Processing, National Tsing-Hwa University, Taiwan. pp. 87-110.
Chung, Siaw-Fong, Chu-Ren Huang and Kathleen Ahrens. 2003b. “ECONOMY IS A
TRANSPORTATION_DEVICE: Contrastive Representation of Source Domain Knowledge in English
and Chinese.” In Proceedings of the special session of Upper Ontologies and Natural Language
Processing (UONLP), 2003 International Conference on Natural Language Processing and Knowledge
Engineering (NLP-KE), Beijing, China. pp. 790-796.
Lakoff, George and Mark Johnson. 1980. Metaphors We Live By. Chicago and London: The University of
Chicago Press.
Huang, Chu-Ren, Ru-Ying Chang, Shiang-Bin Lee. 2004. “Sinica BOW (Bilingual Ontological Wordnet):
Integration of Bilingual WordNet and SUMO.” In the Proceedings of the 4th International Conference on
Language Resources and Evaluation (LREC2004). Lisbon. Portugal.
Niles, I. and A Pease. 2003. “Linking Lexicons and Ontologies: Mapping WordNet to the Suggested Upper
Merged Ontology.” In Proceedings of the 2003 International Conference on Information and Knowledge
Engineering,, Las Vegas, Nevada.

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