Specialized Corpora for Translators: A Quantitative Method to Determine Representativeness
By Gloria Corpas Pastor, Ph.D.
and
Miriam Seghiri, Ph.D.,
University of Málaga,
Spain
gcorpas[at]uma.es
seghiri[at]uma.es
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A Quantitative Method to Determine
Representativeness1
 
1. Introduction
Nowadays, there can be no doubt as to the
importance or the necessity of using corpora in translation.
Equally, given the short deadlines and speed that are now
demanded in the translation industry, the virtual corpus
has undeniably proved itself a most useful tool. Many authors
have explored the possibilities offered by corpora for specialized
language teaching and translation (cf. Bernardini and Zanettin,
2000; Corpas, 2001 and 2004, Bowker and Pearson, 2002, to
name but a few). Ad-hoc, specialized corpora mined from
electronic resources available on the Internet have proved
to be a first-class documentary resource, as well as a valuable
tool in decision-making and in revision. However, there
is a surprising scarcity of studies devoted to analyzing
the quality of the corpora that are being used in translation.
There are countless projects of studies
based on corpora which rely on the quality and representativeness
of each corpus as their foundation for producing valid results.
As Biber has pointed out, "the representativeness of
the corpus, in turn, determines the kinds of research questions
that can be addressed and the generalizability of the results
of the research" (Biber et al. 1988: 246). However,
despite agreement as to their importance (cf. Biber 1988,
1990, 1993, 1994 and 1995; Atkins, Clear and Ostler 1992;
Quirk 1992 or EAGLES 1994, 1996a and 1996b),
these concepts continue to be very vague and seemingly no
consensus exists:
"Several corpus linguists have raised issues
concerning the size and representativeness of specialised
corpora as well as the generalizability of their findings.
In fact, these are thorny issues which have also been widely
debated in the literature on corpus studies in general,
and to which there seem to be no easy answers." (Flowerdale,
2004: 18)
So, in this paper we will describe a method2
to assess the quality of a corpus in terms of representativeness.
By using the N-Cor algorithm it is possible to quantify
a posteriori, for the first time, the minimum
number of documents and words that should be included in
a specialized language corpus, in order that it may be considered
representative. A computer application has been implemented
that automatically determines the representativeness threshold
for any given corpus. In the present paper this software
will be used with a sample corpus of general conditions
in vacation package contracts (English-Spanish) mined from
the Internet3.
2. Corpus minimum size
The size of the corpus is a decisive
factor in determining whether the sample is representative
in relation to the needs of the research project (Lavid,
2005). However, even today the concept of representativeness
is still surprisingly imprecise considering its acceptance
as a central characteristic that distinguishes a corpus
from any other kind of collection. 4
As Biber, who is one of the most prolific writers on the
subject of corpus representativeness, emphasizes, "a
corpus is not simply a collection of texts. Rather, a corpus
seeks to represent a language or some part of a language"
(Biber et al. 1998: 246). Nevertheless, at the same time
Biber remains conscious of the difficulties involved in
compiling a corpus that could be defined as "representative"
(cf. Biber et al. 1998: 246-247).
It is therefore commonplace to come up against
questions over the minimum number of texts that will guarantee
that the sample taken is scientifically valid, as well as
debates over how to specify from what quantity it is possible
to decide that the number of texts included, and therefore
the number of words, is sufficient (Sanahuja and Silva 2001).
There have been many attempts to set the
size, or at least establish a minimum number of texts, from
which a specialized corpus may be compiled. Some of the
most important are those put forward by Heaps (1978), Young-Mi
(1995) and Sánchez Pérez and Cantos Gómez
(1997). However, subsequently some of these authors such
as Cantos (Yang et al. 2000: 21) recognized some shortcomings
in these works, stating that it might be attributed to their
preference for Zipf's law. Zipf's law can give us an idea
of the breadth of vocabulary used, but it is not limited
to a particular or approximate number because this will
depend on how the constant is determined (Braun 2005 [1996]
and Carrasco Jiménez 2003: 3). Numerous studies have
been based on that law, but the conclusions they reach do
not specify, even through the use of graphs, the number
of texts that are necessary to compile a corpus for a particular
specialized field.
A possible solution could be to analyze
the lexical density of a corpus in relation to the increase
in documentary material included (Corpas Pastor and Seghiri
Domínguez, 2006, and Seghiri Domínguez, 2006).
In other words, if the ratio between the actual number of
different words in a text and the total number of words
(types/tokens) is an indicator of lexical density or richness,
it may be possible to create a formula that can represent
increases in the corpus (C) on a document by document (d)
basis: the number of types does not increase in proportion
to the number of words the corpus contains, once a certain
number of texts has been achieved.
|
Cn = d1 + d2 + d3 + ... + dn |
This may make it possible to determine the minimum size
of a corpus and the quantity that must be reached for it
to begin to be representative. With the help of graphs,
it should be possible to establish whether the corpus is
representative and approximately how many documents are
necessary to achieve this. This theory has become a practical
reality in the shape of a software application (ReCor5)
which enables accurate evaluation of corpus representativeness.
Once the question of quality is ensured in terms of corpus
design and document selection, this program can be used
to determine a posteriori whether the size reached
by a given corpus is sufficiently representative of this
particular sector of the tourist industry.
For illustrative purposes, a sample corpus composed of
general conditions for vacation packages in Spanish and
English has been used. The importance of this text type,
dealing with vacation packages, is clear because, alongside
contracts for time-shares, it is the only type of tourism
contract that is covered by substantive communitary legislation.
Also, since the Spanish tourist industry is one of the main
driving forces behind the Spanish economy,6
there is a large demand in the tourism sector for translations
of general conditions of vacation packages both from Spanish
into English and from English into Spanish (cf. ACT, 2005).
This the component of general conditions for vacation packages
will be relatively limited as it will be used by a very
specific community in a concrete communication situation,
the sale of vacation packages. In addition, the general
conditions constitute an excellent text type, since by law
(cf. Council Directive of 13 June 1990 on package travel,
vacation packages and package tours regulations, 90/314/EEC)
they must appear in the brochures that vacation package
companies produce for advertising purposes.
3. The software
In order to quantify corpus representatives,
a software program has been implemented. ReCor's
interface is simple, intuitive, and user-friendly (cf. Fig.
1). First, an input file may be selected; this could be
anything from a particular clause in a policy to the entire
corpus. There is also an option: "Input File (Words
Filter)," which filters out all those words that
the user wants to exclude from the analysis, like addresses,
proper names or even HTML tags, in the case where the corpus
has not been cleaned." Next, three output files are
created. The first, "Statistical Analysis,"
collates the results from two distinct analyses; first,
with the files ordered alphabetically by name and then with
the files in random order. The document that appears is
structured into five columns which show the number of types,
the number of tokens, the ratio between the number of different
words and the total number of words (types/tokens), the
number of words that appear only once (V1) and the number
of words that appear only twice (V2). The second output
file, "Alphabetical Order," generates two
columns; the first shows the words in alphabetical order
with their corresponding number of occurrences appearing
in the second column. The same information is shown in the
third file, "Frequency," but this time
the words are ordered according to their frequency or rank.
The application also allows the user to work with groups
of up to ten words (n-grams)7
and phraseology, as well as allowing numbers to be filtered
out.
Figure 1: The ReCor interface.
3.1. Graphical representation of data
The program illustrates the level of representativeness
of a corpus in a simple graph form, which shows lines that
grow exponentially at first and then stabilize as they approach
zero. It should be noted here that zero (= 0) is unachievable
because of the existence in the text of variables that are
impossible to control such as addresses, proper names or
numbers, to name only some of those more frequently encountered.
In the first presentation of the corpus in graph form that
the programme generates—Graphical Representation
A—the number of files selected is shown on the
horizontal axis, while the vertical axis shows the types/tokens
ratio. The results of two different operations are shown,
one with the files ordered alphabetically (the red line),
and the other with the files introduced at random (the blue
line). In this way the program double-checks to verify that
the order in which the texts are introduced does not have
repercussions on the representativeness of the corpus. Both
operations show an exponential decrease as the number of
texts selected increases. However, at the point where both
the red and blue lines stabilize, it is possible to state
that the corpus is representative, and at precisely this
point it is possible to see approximately how many texts
will produce this result.
At the same time another graph—Graphical Representation
B—is generated in which the number of tokens is
shown on the horizontal axis. This graph can be used to
determine the total number of words that should be set for
the minimum size of the collection.
Once these steps have been taken, it is possible to check
whether the number of general conditions of a travel package
that have been compiled in the two languages involved—English
and Spanish—is sufficient to enable us to affirm that
our sample corpus is representative. See Figures 2 and 3
below which show the representativeness of the two languages
involved.
Figure 2: Representativeness of the Spanish
subcorpus (1- gram).
Figure 3: Representativeness of the English
subcorpus (1-gram).
From the data shown in Figure 2 it is possible to deduce
that, according to Graph A, the component of general conditions
in Spanish begins to be representative from the point of
the inclusion of 200 documents; since the curve hardly varies
either before or after this number, in other words this
is the point where the lines stabilize and are closest to
zero. As mentioned above, in practice zero is unattainable
because, despite having chosen ReCor's option to
filter out numbers as well as using the word filter, all
documents always contain a number of variables which are
impossible to control (for example, proper names or addresses,
to mention only some of the more frequent examples). Graph
B shows the minimum total number of words (tokens) necessary
for the corpus to be considered representative, which in
this case is 750,000 words.
In the case of Figure 3, from Graph A it is possible to
assert that the English subcorpus becomes representative
from the point where 175 documents are included. In addition,
according to the data generated by ReCor shown in
Graph B, the figure for the total number of words necessary
in order to claim representativeness is around 600,000 words.
A comparison of the two sets of graphs in Figures 2 and
3 shows that despite the fact that a similar number of general
conditions have been found on the Internet for both languages—279
texts in Spanish and 240 in English—the English documents
reach the point of representativeness long before the Spanish
documents: 175 documents and 600,000 words in English against
200 documents and 750,000 words in Spanish.
The results remain largely the same even when the analysis
is performed on a two-word basis (2-grams): 225 documents
and 750,000 words in English (cf. Figure 5) as against 250
documents and 800,000 words in Spanish (cf. Figure 4).
Figure 4: Representativeness of the Spanish
subcorpus (2- grams).
Figure 5: Representativeness of the English
subcorpus (2- grams).
From this it may therefore be deduced that,
despite the fact that the legal systems involved in the
study all have substantive legislation on the subject of
vacation packages, the English general conditions tend to
be more homogeneous than those in Spanish. In other words,
it is possible to infer that the general conditions in English
present super-, macro- and microstructures that are very
similar to each other and use a narrower terminological
range.
Despite these quantitative differences,
however, it is not possible to determine a priori
the exact total number of words or documents that should
be included in specialized language corpora (which in general
tend to be smaller) in order that they may be considered
representative. This is because, as has been illustrated,
size will be determined according to the language and text
types, as well as the restrictions of a particular specialized
field or diatopic limitations.
4. Conclusion
Now, for the first time, corpus representativeness
can be measured a posteriori by means of the N-Cor
algorithm. ReCor is a computer application based
on the N-Cor algorithm that calculates the minimum number
of documents and words that should be included in specialized
language corpora, in order that they may be considered representative.
It should be pointed out that it is not possible to establish
the minimum number of documents for a given corpus a
priori, as the size will depend on the language and
genres involved, as well as on the restrictions of a particular
specialized field and any other diasystematic limitations.
This new quantitative method will make exciting future research
for collocational and phraseological studies on corpus representativeness
possible.
References
ACT. 2005. Primer estudio de mercado de los servicios
de traducción profesional en España de la
Asociación de Empresas de Traducción (ACT).
Madrid: ACT.
Atkins, S. Clear, J. and Ostler, N. 1992. "Corpus
Design Criteria." Literary and Linguistic Computing
7 (1): 1-16.
Biber, D. 1988. Variation across Speech and Writing.
Cambridge: Cambridge University Press.
Biber, D. 1990. "Methodological Issues Regarding Corpus-based
Analyses of Linguistic Variations." Literary and
Linguistic Computing 5: 257-269.
Biber, D. 1993. "Representativiness in Corpus Design."
Literary and Linguistic Computing 8 (4): 243-257.
Biber, D. 1994. "Representativeness in Corpus Design."
In Current Issues in Computational Linguistics: In Honour
of Don Walker, A. Zampolli, N. Calzolari and M. Palmer
(eds), 377-408. Dordrech and Pisa: Kluwer and Giardini.
Biber, D. 1995. Dimensions of Register Variation: A
cross-linguistic comparison. Cambridge: Cambridge University
Press.
Biber, D., Conrad, S. and Reppen, R. 1998. Corpus Linguistics:
Investigating Language Structure and Use. Cambridge:
Cambridge University Press.
Bowker, L. and Pearson, J. 2002. Working with Specialized
Language: A practical guie to using corpora. Londres:
Routledge.
Braun, E. 2005 [1996]. "El caos ordena la lingüística.
La ley de Zipf." In Caos fractales y cosas raras,
E. Braun (ed). Mexico D.F.: Fondo de Cultura Económica.
http://omega.ilce.edu.mx:3000/.../caos.htm
[10/06/2007].
Carrasco Jiménez, R. C. 2003. La ley de Zipf
en la Biblioteca Miguel de Cervantes. Alicante: Universidad
de Alicante. http://www.dlsi.ua.es/asignaturas/aa/Zipf.pdf
[10/06/2007].
Corpas Pastor, G. 2001. "Compilación de un
corpus ad hoc para la enseñanza de la traducción
inversa especializada." TRANS. Revista de Traductología
5: 155-184.
Corpas Pastor, G. 2002. "Traducir con corpus: de la
teoría a la práctica." In Texto, terminología
y traducción, J. García Palacios and M.
T. Fuentes (eds.), 189-226. Salamanca: Almar.
Corpas Pastor, G. 2004. "Localización de recursos
y compilación de corpus vía Internet: Aplicaciones
para la didáctica de la traducción médica
especializada." In Manual de documentación
y terminología para la traducción especializada,
C. Gonzalo García and V García Yebra (eds.),
223-257. Madrid: Arco/Libros.
Corpas Pastor, G. and Seghiri Domínguez, M. 2006.
El concepto de representatividad en la Lingüística
del Corpus: aproximaciones teóricas y metodológicas.
Technical document BFF2003-04616 MCYT/TI-DT-2006-1.
Council Directive of 13 June 1990 on package travel, vacation
packages and package tours regulations, 90/314/EEC
EAGLES. 1994. "Corpus Typology: A framework for classification."
EAGLES Document 080294. 1-18.
EAGLES. 1996a. "Text corpora Working Group
reading Guide." EAGLES Document EAG-TCWG-FR-2. http://www.ilc.cnr.it/EAGLES/corpintr/corpintr.html
[accessed: 10/06/2007].
EAGLES. 1996b. Preliminary Recommendations on
Corpus Typology. EAGLES Document EAG-TCWG-CTYP/P.
http://www.ilc.cnr.it/EAGLES96/corpustyp/corpustyp.html
[accessed: 10/06/2007].
Flowerdale, L. 2004. "The argument for using English
specialised corpora to un academic and professional language."
In Discourse In The Professions: Perspectives From Corpus
Linguistics, U. Connor and T. Upton, (eds.), 11-33.
Amsterdam/Philadelphia: John Benjamins.
Giouli, V. and Piperidis, S. 2002. Corpora and HLT.
Current trends in corpus processing and annotation.
Bulgaria: Insitute for Language and Speech Processing. http://www.larflast.bas.bg/balric/eng_files/corpora1.php
[10/06/2007].
Heaps, H. S. 1978. Information Retrieval: Computational
and Theoretical Aspects. New York: Academic Press.
Lavid López, J. 2005. Lenguaje y nuevas tecnologías:
nuevas perspectivas, métodos y herramientas para
el lingüista del siglo XXI. Madrid: Cátedra.
Quirk, R. 1992. "On Corpus Principles and Design."
In Directions in Corpus Linguistics. Proceedings of Nobel
Symposium 82, Stockholm, 4-8 August 1991, J. Svartvik
(ed), 457- 469. Berlin/NewYork: Mouton de Gruyter.
Sanahuja, S. and Silva, A. 2001. "Muestreo teórico
and estudios del discurso. Una propuesta teórico-metodológica
para la generación de categorías significativas
en el campo del Análisis del Discurso." El
Estudio del Discurso: Metodología Multidisciplinaria.
II Coloquio Nacional de Investigadores en Estudios del Discurso.
La Plata, 6 al 8 de septiembre de 2001. Buenos Aires:
Asociación Latinoamericana de Estudios del Discurso
and Universidad Nacional del Centro de la Provincia de Buenos
Aires. http://www.sai.com.ar/KUCORIA/discurso.html
[10/06/2007].
Sánchez Pérez, A. and Cantos Gómez,
P. 1997. "Predictability of Word Forms (Types) and
Lemmas in Linguistic Corpora. A Case Study Based on the
Analysis of the CUMBRE Corpus: An 8-Million-Word Corpus
of Contemporary Spanish." International Journal
of Corpus Linguistics 2 (2): 259-280.
Seghiri, M. 2006. Compilación de un corpus trilingüe
de seguros turísticos (español-inglés-italiano):
aspectos de evaluación, catalogación, diseño
y representatividad. PhD Thesis. Málaga: Universidad
de Málaga. (CD-Rom edition).
WTTC. 2006a. World Travel and Tourism climbing
to new heights. The 2006 Travel & Tourism Economic Research.
Londres: World Travel & Tourism Council. http://www.wttc.org/2006TSA/pdf/World.pdf
[accessed: 30/04/2006].
WTTC. 2006b. United Kingdom Travel and Tourism
climbing to new heights. The 2006 Travel & Tourism Economic
Research. Londres: World Travel & Tourism Council.
http://www.wttc.org/2006TSA/pdf/United%20Kingdom.pdf
[accessed: 30/04/2006].
WTTC. 2006c. Ireland Travel and Tourism climbing
to new heights. The 2006 Travel & Tourism Economic Research.
Londres: World Travel & Tourism Council. http://www.wttc.org/2006TSA/pdf/Ireland.pdf
[accessed: 30/04/2006].
WTTC. 2006d. Spain Travel and Tourism climbing
to new heights. The 2006 Travel & Tourism Economic Research.
Londres: World Travel & Tourism Council. http://www.wttc.org/2006TSA/pdf/Spain.pdf
[accessed: 30/04/2006].
Yang, D., Cantos Gómez, P. and Song, M. 2000. "An
Algorithm for Predicting the Relationship between Lemmas
and Corpus Size." ETRI Journal 22 (2): 20-31.
http://etrij.etri.re.kr/Cyber/servlet/GetFile?fileid=SPF-1042453354988
[accessed: 10/06/2007].
Young-Mi Jeong. 1995. «Statistical Characteristics of Korean
Vocabulary and Its Application». Lexicographic Study.
5 (6). 134-163.
1
The research reported in this paper has been carried out in
the framework of R&D Project for Excelence La contratación
turística electrónica multilingüe como
mediación intercultural: aspectos legales, traductológicos
y terminológicos [Multi-lingual tourism e-contracts:
legal, translational and terminological aspects]. Funding
source: Andalusian Ministry of Education, Science and
Technology. Ref. no. HUM-892 (2006-2009).
2
The methodology we describe in this paper has been awarded
the 2007 Translation Technologies Research Award
(Premio de Investigación en Tecnologías
de la Traducción) by the Translation Technologies
Watch (Observatorio de Tecnologías de la Traducción).
Further information at the URL: http://www.uem.es/web/ott.
The ReCor program (version 3.0) will be soon available at:
http://www.recorweb.com.
3
A systematic methodology for corpus compilation based on electronic
resources available on the Internet is described in Corpas
(2002) and Seghiri (2006).
4
There are a surprising number of research projects that, whilst
endeavoring to compile a "representative" corpus,
hardly seem to touch on this concept. Usually, it is noticeable
that the availability of material in the particular field
of study determines the final size of the corpus (Giouli y
Piperidis, 2002).
5
ReCor is an acronym derived from the function it was
designed for: the representation of corpora.
6
Tourism is responsible for a huge volume of business in the
international economy with Europe occupying a privileged position
at the top of the world scale. In 2006 Europe generated $6,466.2
billion in this sector, equivalent to 10.3% of the world's
gross domestic product (GDP), forecast to rise to 11% by 2011,
accounting for 8.7% of total employment (WTTC, 2006a).
Also see studies by the WTTC concerning the United Kingdom
(2006b), Ireland (2006c) and Spain (2006d)
for a more detailed analysis of the figures for these countries
in this sector.
7
In this study we used version 2.1 of ReCor. We are
currently working on a new version (ReCor 3.0) which has an
improved capacity for working with multiple and very large
files quickly and also allows lexical bundles to be identified
on the basis of analysis of n-grams (n ≥ 1 and n ≤
10) of the corpus.
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