6 Language corpora
Liang Maocheng
I. overvie w & history

An Introduction
IV. Methods and Testing

Traditional Thoughts of Education

Research M ethods

Foreign Language Education

Language Testing.

(Pedagogical) Lexicography
V. Learning
II. Lg Description

Language Descriptions

Language Corpora.

Stylistics.

Discourse Analysis. vs CA

Second Language Learning.

Individual Differences in Second
Language Learning.
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Social Influences on Language
Learning.
VI. Teaching
III. Cognitive & Social

Fashions in Language Teaching

Language Acquisition: L1 vs L2

Language, Thought, and Culture.

Language and Gender.

Language and Politics.

Language Teacher Education.

World Englishes.

The Practice of LSP

Bilingual Education.
M ethodology.

Computer Assisted Language
Learning
Fig. 0 A Bird’s-Eye-Vie w of Applied Linguistic Studies
7.1 Introduction
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7.1 Introduction
7.2 Empiricism, corpus linguistics, and electronic corpora
7.3 Applications of corpora in applied linguistics
7.3.1 An overview of the use of corpora in applied linguistics
7.3.2 The Lexical Syllabus: a corpus-based approach to
syllabus designing
7.3.3 Data-Driven Learning (DDL)
7.3.4 Corpora in language testing
7.3.5 Corpus-based interlanguage analysis
7.4 Future trends
7.1 Introduction
• The debates about whether corpus linguistics should be
defined as a new discipline, a theory or a methodology.
• Some linguists working with corpora tend to think that corpus
linguistics goes well beyond the purely methodological role by
re-uniting the activities of data gathering and theorizing, thus
leading to a qualitative change in our understanding of
language (Halliday, 1993:24),
• More researchers (e.g., Botley & McEnery, 2000; Leech, 1992;
McEnery & Wilson, 2001: 2; Meyer, 2002:28; ) seem to share
the view that corpus linguistics is a methodology contributing
to many hyphenated disciplines in linguistics. This
methodological nature has brought about the convergence of
corpus linguistics with many disciplines, of which applied
linguistics probably enjoys the most benefits corpus linguistics
can offer.
2. Empiricism, corpus linguistics,
and electronic corpora
• The 1950s witnessed the peak of empiricism in linguistics,
with Behaviorists dominating American linguistics, and Firth, a
leading figure of British linguistics at the time, forcefully
advocating his data-based approach with his well-cited belief
that “You shall know a word by the company it keeps” (Firth,
1957).
• However, with the quick advent of generative linguistics in the
late 1950s, empiricism gave way to Chomskyans, whose
approach is based on the intuition of ‘ideal speakers’ rather
than collected performance data. It was not until the 1990s
that there was again a resurgence of interest in the empiricist
approach (Church & Mercer, 1993).
• Corpus linguistics is empirical. Its object is real language data
(Biber et al., 1998; Teubert 2005). In fact, there is nothing new
about working with real language data. In as early as the
1960s, when generative linguistics was at its peak, Francis and
Kucera created their one-million-word corpus of American
written English at Brown University (Francis and Kucera, 1982).
• Then, in the 1970s, Johansson and Leech at the University of
Oslo and the University of Lancaster compiled the LancasterOslo/Bergen Corpus (LOB), the British counterpart of the
American Brown Corpus.
• Since then, there has been a boom of corpus compilation.
Spoken corpora (such as the London-Lund Corpus, see
Svartvik, 1990) and written corpora, diachronic corpora (such
as ICAME) and synchronic corpora, large general corpora
(such as the BNC and the Bank of English) and specialized
corpora, native speakers’ language corpora and learner
corpora and many other types of corpora were created one
after another.
• These English corpora are constantly providing data to meet
the various needs of applied linguistics.
• After a few decades of world-wide corpusrelated work, several tendencies now seem to
be obvious in corpus building in the new
empiricist age.
• First, modern corpora are becoming ever
larger and more balanced, and therefore often
claimed to be more representative of the
language concerned.
• Second, there is often the need for rationalism
and empiricism to work together.
• Third, many types of specialized language
corpora are being built to serve specific
purposes.
• Fourth, many researchers in applied linguistics
have realized the usefulness of corpora for the
analysis of learner language.
• Finally, as corpus annotation is believed to
bring “added value” (Leech, 2005) to a corpus,
there is a tendency that corpus annotations
are becoming more refined, providing detailed
information about lexical, phraseological,
phonetic, prosodic, syntactic, semantic, and
discourse aspects of language.
7.3 Applications of corpora in
applied linguistics
• 7.3.1 An overview of the use of corpora in
applied linguistics
• 7.3.2 The Lexical Syllabus: a corpus-based
approach to syllabus designing
• 7.3.3 Data-Driven Learning (DDL)
• 7.3.4 Corpora in language testing
• 7.3.5 Corpus-based interlanguage analysis
• In order to provide an overall picture of the extensive
use of corpora in applied linguistics, this section will
first present an overview of the use of corpora in
applied linguistics.
• Following the overview, our discussion will focus on a
few major areas of applied linguistics where corpora
are playing increasingly important roles, namely,
syllabus design, data-driven learning, language
testing, and interlanguage analysis.
3.1 An overview of the use of
corpora in applied linguistics
• Leech (1997) summarizes the applications of
corpora in language teaching with three
concentric circles.
Direct use of corpora in teaching
Use of corpora indirectly applied to teaching
Further teaching-oriented corpus development
Figure 1: The use of corpora in language teaching
(from Leech, 1997)
• Drawing on Fligelstone (1993), Leech (1997) claims that ‘the
direct use of corpora in teaching’ (the innermost circle)
involves teaching about [corpora], teaching to exploit
[corpora], and exploiting [corpora] to teach. According to
Leech (1997), teaching about corpora refers to the courses in
corpus linguistics itself; teaching to exploit corpora refers to
the courses which teach students to exploit corpora with
concordance programs, and learn to use the target language
from real-language data (hence data-driven learning, to be
discussed in more detail later in this section). Finally,
exploiting corpora to teach means making selective use of
corpora in the teaching of language or linguistic courses which
would traditionally be taught with non-corpus methods.
• The more peripheral circle, ‘the use of corpora indirectly
applied to teaching’, according to Leech (1997), involves the
use of corpora for reference publishing, materials
development, and language testing. In reference building, the
Collins (now HarperCollins), Longman, Cambridge University
Press, Oxford University Press and many other publishers have
been actively involved in the publication of corpus-based
dictionaries, electronic corpora, and other language reference
resources, especially those in the electronic form.
• As Hunston states, “corpora have so revolutionised the writing of
dictionaries and grammar books for language learners that it is by now
virtually unheard-of for a large publishing company to produce a learner’s
dictionary or grammar reference book that does not claim to be based on
a corpus” (Hunston, 2002:96) (For more detailed accounts of the use of
corpora in dictionary writing, see Sinclair, 1987; Summers, 1996). In
materials development, increasing attention is paid to the use of corpora
in the compilation of syllabuses (to be discussed later in this section) and
other teaching materials. Also in the second of the three concentric circles
is language testing (See Section 3.4 for more detail), which “benefits from
the conjunction of computers and corpora in offering an automated,
learner-centred, open-ended and tailored confrontation with the wealth
and variety of real-language data”.
• Finally, the outermost circle, ‘further teaching-oriented corpus
development’, involves the creation of specialized corpora for
specific pedagogical purposes. To illustrate the need to build
such specialized corpora, Leech (1997) mentions LSP
(Language for Specific Purposes) corpora, L1 and L2
developmental corpora (also an important data source for
Second Language Acquisition research), and bilingual/
multilingual corpora. Leech (1997) believes these are
important resources that language teaching can benefit from.
• Of course, Leech’s (1997) discussion focuses on the
applications of corpora in language teaching. Second
Language Acquisition research, one of our major concerns in
this book, is not as important for him. In fact, corpus-based
approaches to SLA studies, particularly to interlanguage
analysis, have become one of the most prevalent research
methodologies for the analysis of learner language. This issue
will be brought to a more detailed discussion later in the
chapter.
3.2 The Lexical Syllabus: a corpusbased approach to syllabus designing
• The notion of a ‘lexical syllabus’ was originally
proposed by Sinclair and Renouf (1988), and
further developed by Willis (1990), who points
out a contradiction between syllabus and
methodology in language teaching:
• The process of syllabus design involves itemising language to
identify what is to be learned. Communicative methodology
involves exposure to natural language use to enable learners
to apply their innate faculties to recreate language systems.
There is an obvious contradiction between the two. An
approach which itemises language seems to imply that items
can be learned discretely, and that the language can be built
up from an accretion of these items. Communicative
methodology is holistic in that it relies on the ability of
learners to abstract from the language to which they are
exposed, in order to recreate a picture of the target language.
(Willis, 1990: viii)
• The lexical syllabus attempts to reconcile the
contradiction. Rather than relying on a clear
distinction between grammar and lexis, the
lexical syllabus blurs the distinction and builds
itself on the notion of phraseology.
• As early as a few decades ago, some researchers (e.g.,
Nattinger and DeCarrico, 1989; 1992; Pawley & Syder, 1983)
came to realize that phrases (also called ‘lexical phrases’,
‘lexical bundles’, ‘lexical chunks’, ‘formulae’, ‘formulaic
language’, ‘prefabricated routines’, etc.) are frequently used
and are therefore important for the teaching and learning of
English.
• Later researchers such as Cowie (1998), Skehen (1998) and
Wray (2002) are also convinced that a better command of
such phrases can improve the fluency, accuracy and
complexity of second language production.
• Unfortunately, most syllabuses for ELT are based on
traditional descriptive systems of English, which consist of
grammar and lexis (Hunston, 2002). Such descriptive systems,
according to Sinclair (1991), fail to give a satisfactory account
of how language is used.
• Besides, combining the building blocks (lexis) with grammar
does not result in a good account of the phrases in a
language.
• Against this background, Sinclair (1991) proposes a new
descriptive system of language, completely denying the
distinction between grammar and lexis and putting
phraseology at the heart of language description.
• The lexical syllabus is not a syllabus consisting of vocabulary items. It
comprises phraseology, which “encompasses all aspects of preferred
sequencing as well as the occurrence of so-called ‘fixed’ phrases”
(Hunston, 2002:138).
• Such a syllabus, according to Hunston (2002), differs from a conventional
syllabus only in that the central concept of organization is lexis. To put it
simply, as a relatively small number of words in English account for a very
high proportion of English text, it makes sense to teach learners the most
frequent words in the target language.
• However, as learning words out of their context does not seem to be a
good idea, a syllabus had better not be designed in such a way that it only
specifies the vocabulary items to be learned. Rather than the lexis of the
target language, phraseology (words with their most frequently used
patterns) is what is to be specified in detail in a lexical syllabus.
• According to Sinclair & Renouf (1988), ‘the
main focus of study should be on
• (a) the commonest word forms in the
language;
• (b) the central patterns of usage;
• (c) the combinations they usually form’
(1988:148).
• This is exactly what is covered in Willis’ (1990)
syllabus.
• Willis (1990) argues that the lexical syllabus effectively addresses “the
main focus of study” mentioned in Sinclair & Renouf (1988): (a) Willis’
(1990) syllabus consists of three different levels.
• Level I covers the most frequent 700 words in English, which, according to
Willis (1990), make up around 70% of all English text.
• Level II covers the most frequent 1,500 and
• Level III covers the most frequent 2,500 words in English.
• (b) The lexical syllabus illustrates the central patterns of usage of the most
frequent words in English. Such patterns of usage were later developed
into a system of grammar called “pattern grammar” (Hunston & Francis,
2000).
• (c) Typical combinations of word forms (collocations) from authentic
language are highlighted to provide information about the phraseology of
the words concerned.
• It can be seen that the lexical syllabus is not a syllabus which
lists all the vocabulary items learners are required to learn.
• To Willis (1990), “English is a lexical language”, meaning that
“many of the concepts we traditionally think of as belonging
to ‘grammar’ can be better handled as aspects of vocabulary”
(Hunston, 2002:190). Conditionals, for example, could be
handled by looking at the hypothetical meaning of the word
‘would’.
• The most productive way to interpret grammar, therefore, is
as lexical patterning, rather than as rules about the sequence
of words that often do not work.
• However, the lexical syllabus, as proposed in Willis (1990), is
not without challenges.
• First, frequency is a useful factor to take into consideration
when a syllabus is designed. However, language learning is
not that simple. Native language influence, cultural difference,
learnability, usefulness, and many other factors can all bring
about some difficulty for language learning.
• As Cook (1998:62) notes, “an item may be frequent but
limited in range, or infrequent but useful in a wide range of
contexts”. Proponents of the Lexical Syllabus seem to have
ignored this fact. They believe their syllabus will work for
learners of all linguistic and ethnic backgrounds. To many
researchers and practitioners of applied linguistics, such a
belief is too simplistic to account for complicated processes
such as language learning.
• Second, it is not an easy task to select a manageable yet meaningful
number of items from the entire vocabulary of a language for inclusion in
a lexical syllabus.
• While it may be true that the 2,500 words in Willis’ syllabus can account
for 80% of all English text, knowing 80% of the words in a text does not
guarantee good comprehension. In fact, most of the 80% are functional
words or delexicalized words, which do not have much content. In other
words, the role of the commonest words in language comprehension and
production may not be as significant as the high frequency counts suggest.
Also, while it may be relatively easy to include a content word as an entry
in a lexical syllabus, functional words can be a big problem. Such a word
entry may take tens of pages long in the lexical syllabus. If the most
frequent words are to be accounted in detail, it is very likely that the
syllabus will become a comprehensive guide to the usage of functional
words.
• Finally, the size of the syllabus may also be a problem. Willis
(1990), when creating his “elementary syllabus”, had to work
with huge amounts of data. He complained that “The data
sheets for Level I alone ran to hundreds of pages which we
had to distil into fifteen units” (Willis, 1990:130).
• We just cannot help wondering how many thousands of data
sheets would have to be created if a syllabus had to be
written for advanced learners.
3.3 Data-Driven Learning (DDL)
• The use of corpora in the language teaching
classroom owes much to Tim Johns (Leech, 1997),
who began to make use of concordancers in the
language classroom in as early as the 1980s. He then
wrote a concordance program himself, and later
developed the concept of Data-Driven Learning (DDL)
(See Johns, 1991).
• DDL is an approach to language learning by which the learner
gains insights into the language that she is learning by using
concordance programs to locate authentic examples of
language in use. The use of authentic linguistic examples is
claimed to better help those learning English as a second or
foreign language than invented or artificial examples (Johns,
1994).
• These authentic examples are believed to be far better than
the examples the teachers make up themselves, which
unavoidably lack authenticity. In DDL, the learning process is
no longer based solely on what the teacher expects the
learners to learn, but on the learners’ own discovery of rules,
principles and patterns of usage in the foreign language.
• Drawing from George Clemenceau’s (1841-1929) claim that ‘War is much
too serious a thing to be left to the military’ (quoted in Leech, 1997),
Johns believes that ‘research is too serious to be left to the researchers’
(Johns, 1991:2), and that research is a natural extension of learning.
• The theory behind DDL is that students could act as ‘language detectives’
(Johns, 1997:101), exploring a wealth of authentic language data and
discovering facts about the language they are learning.
• In other words, learning is motivated by active involvement and driven by
authentic language data, whereas learners become active explorers rather
than passive recipients. To a great extent, this is very much like
researchers working in the scientific laboratory, and the advantage lies in
the direct confrontation with data (Leech, 1997).
• Murison-Bowie, the author of the MicroConcord Manual,
gives some very persuasive reasons for using a concordancer:
• … any search using a concordancer is given a clearer focus if one starts out
with a problem in mind, and some, however provisional, answer to it. You
may decide that your answer was basically right, and that none of the
exceptions is interesting enough to warrant a re-formulation of your
answer. On the other hand, you may decide to tag on a bit to the answer,
or abandon the answer completely and to take a closer look. Whichever
you decide, it will frequently be the case that you will want to formulate
another question, which will start you off down a winding road to who
knows where.
(Murison-Bowie, 1993:46, cited in Rezeau, 2001:153)
• It can be seen that in DDL, language learning is a hypothesistesting process, in which whenever the learner has a
question, she goes to the concordancer for help. If what she
discovers coincides with the authentic language data shown in
the concordance lines, her hypothesis is tested to be true, and
her knowledge of the language is reinforced; conversely, if the
concordance data contradict her hypothesis, she modifies it
and comes closer to how the language should be used.
• Higgins (1991) mentions that classroom
concordancing tends to have two
characteristic objectives:
• “using searches for function words as a way
of helping learners discover grammatical rules,
and
• searching for pairs of near synonyms in order
to give learners some of the evidence needed
for distinguishing their use” (p.92).
• What the foregoing example shows is that in
data-driven learning, the learner often has a
question in mind. She then goes to explore
the data for an answer. The whole process, as
mentioned before, involves testing a
hypothesis with hands-on activities. In so
doing, the learner is more likely to be
motivated.
• It is worth mentioning that many of the features of DDL fit
exactly into the constructivist approach to language teaching,
in which:
1. Learners are more likely to be motivated and actively
involved;
2. The process is learner-centered;
3. Learning is experimentation;
4. Hands-on activities and experiential learning;
5. Teachers work as facilitators;
• As pointed by Gavioli (2005:29), DDL raised several pedagogic
questions, which we have to answer when we encourage our
students to engage themselves in data-driven learning:
1. if learners are to behave as data analysts, what should be the
role of the teacher?
2. the work of language learners is similar to that of language
researchers insofar as “effective language learning is itself a
form of linguistic research” (Johns 1991:2). So, should we ask
the learners to perform linguistic research exactly like
researchers?
3. provided that learners adopt the appropriate instruments and
methodology to actually be able to perform language
research, are the results worth the effort?
• And, to this I would like to add Barnett’s (1993) note:
4. “the use of computer applications in the classroom can easily
fall into the trap of leaving learners too much alone,
overwhelmed by information and resources”.
• What can we do to improve this situation in data-driven
learning?
3.4 Corpora in language testing
• It is only recently that language corpora have begun
to be used for language testing purposes (Hunston,
2002), though language testing could potentially
benefit from the conjunction of computers and
corpora in offering an “automated, learner-centred,
open-ended and tailored confrontation with the
wealth and variety of real-language data” (Leech,
1997).
• In fact, the use of corpora in language testing is such a new
field of studies that Leech (1997) only mentions the
advantages of corpus-based language testing, and predicts
that “automatic scoring in terms of ‘correct’ and ‘incorrect’
responses is feasible”.
• Hunston (2002:205) also states that the work reported in her
book is “mostly in its early stages”.
• Alderson (1996), in a paper entitled “Do corpora have a role in
language assessment?”, has to claim that he could only
“concentrate on exploring future possibilities for the
application of computer-based language corpora in language
assessment” (Alderson, 1996:248).
• To the best of my knowledge, the use of corpora in language
testing roughly falls into two categories. One is the use of
corpora and corpus-related techniques in the automated
evaluation of test questions (particularly subjective questions
like essay questions or short-response questions), and
• the other is the use of corpora to make up test questions
(mostly objective questions).
• The reason behind this is simple: making up subjective
questions does not take a lot of efforts but evaluating them
does; similarly, evaluating objective questions does not take a
lot of efforts but making up these questions may take a lot of
time.
• The earliest attempt to automate essay scoring was made several decades
ago, when Ellis Page and his collaborators devised a system called PEG
(Project Essay Grade) to assign scores for essays written by students (Page,
1968).
• From a collection of human-rated essays, Page extracted 28 text features
(such as the number of word types, the number of word tokens, average
word length, etc.) that correlate with human-assigned essay scores.
• He then conducted a multiple regression, with human-assigned essay
scores as the dependent variable and the extracted text features as
independent variables.
• The multiple regression yielded an estimated equation, which was then
used to predict the scores for new essays.
• Following Page’s methodology, ETS produced another
automated essay scoring system called the E-rater, which has
been used to score millions of essays written in the GMAT,
GRE and TOEFL tests (Burstein, 2003).
• Liang (2005) also made an exploratory study, in which he
extracted 13 essay features to predict scores for new essays.
• All the above-mentioned studies involved the use of corpora
and corpus-related techniques. They reported good
correlations between human-assigned essay scores and
computer-assigned essay scores.
• For more information about automated essay scoring, see
Shermis & Burstein (2003).
• The direct use of corpora in the automatic generation of
exercises (or test questions) is also a relatively new field of
study. To the best of my knowledge, two studies have been
reported in the literature.
• Wojnowska (2002) reported a corpus-based test-maker called
TestBuilder. The Windows-based system extracts information
from a tagged corpus. It can help teachers prepare two types
of single-sentence test items --- gapfilling (cloze) and
hangman. While the system may be useful for English
teachers, the fact that the questions generated with the
system can only be saved in the pure text format greatly
reduces its practicality.
• In other words, the test generated allows no interaction with
the test-taker, and the teacher or the test-taker herself has to
judge the test performance and calculate the scores manually.
Besides, gapfilling and hangman are very similar types of
exercises, both involving the test-taker filling gaps with letters
or combinations of letters (words).
• Obviously, this system has not made full use of the capacity of
the computer and the corpora. Wilson (1997) reported her
study on the automatic generation of CALL exercises from
corpora. Unfortunately, the study has not resulted in a
computer program, and the exercises generated are not
interactive either.
• To take better advantage of corpora for
language testing purposes, we started a
project of our own.
• From this study a Windows-based program
(called CoolTomatoes) has derived, which is
capable of automatically generating
interactive test questions or exercises from
any user-defined corpus.
• CoolTomatoes has a number of useful features.
• Questions are based on real language data. As the corpus
comprises real language data, the test questions generated by
CoolTomatoes are naturally more meaningful. These
questions are more likely to test and enhance test-takers’
ability to use real language to fulfill real-life tasks.
• Question generation is fully customizable. Both the corpus
and the answer options can be customized. The user can
choose to add a new text to an existing corpus, or choose to
load different corpora. This is important because test
questions need to be at different difficulty levels for different
students, and the difficulty of the test questions is determined
by the difficulty of the corpus text. Customizing the answer
options is also important because every teacher/tester almost
always wants to have their own questions (sometimes for
their own students) in the test.
• Interaction with the test-maker. To eliminate chances
of highly similar questions or poor questions due to
an inappropriate corpus, CoolTomatoes allows the
user to preview the questions to be generated. If
necessary, the user can further edit, delete, and add
questions.
• Dual output if needed: The user can choose to
generate a print-out test (for the traditional class) or
an interactive test to be done on a stand-alone
computer or on the web.
• Real-time feedback for the test-takers: In an
interactive test generated by CoolTomatoes, testtakers are given real-time feedback about: 1)
whether a question has been correctly answered; 2)
the overall performance of the test-taker (Figure 3).
• Automatic submission of test performance: If a
webserver and an email address are defined, all testtakers’ test performance data can be automatically
submitted to the email address whereby learning can
be monitored.
• Suggestions
• While the high customizability of CoolTomatoes brings
considerable convenience to the language teacher/tester and
makes learning and testing more of a fun, a few cautions have
to be taken before a test can be released to the end-user
learner.
• First, the difficulty of the corpus on which to base the test
questions has to be carefully monitored. It is, needless to say,
never rewarding to test your students against something that
is far beyond their ability or too easy to be worthwhile.
• Second, the answer options have to be carefully chosen. If an
achievement test is to be made, for example, the answer
options need to include the syntactic and/or lexical focuses
the learners have just been exposed to. If a proficiency test is
to be made, several trials may be necessary to determine
what is going to be in the test. It may be a good idea to
generate a large number of test questions and manually filter
out the less appropriate ones.
• Third, for reasons of content-related language, it is always
necessary to know what is in a test before you ask your
students to do it.
• Finally, there is the danger of overwork on the part of the
students. Learners’ motivation to learn and to be tested
should be cautiously preserved and encouraged. It is never a
good idea to drown them with tons of automatically
generated exercises.
• As seen from our study reported in this section, the
advantages for corpus-based language testing are obvious. As
a corpus comprises collections of real-life language data,
corpus-based language tests or exercises are unmistakably
genuine real-life samples. In the modern age when
authenticity of language use is greatly emphasized in
language testing, corpus-based approaches certainly enjoy
prosperous future.
• The second advantage is that corpus-based language tests can
be automatically graded, since the questions are retrieved
from corpora, and the right answers to these questions are
always readily extractable. Of course, with the questions and
the answers in the corpus, interactive test questions could be
devised.
• One more advantage for corpus-based language tests is speed.
The computer can work faster than the best human assessor if
it is told exactly what to do.
• Of course, a lot more remains to be explored in how language
corpora can be appropriately used in language testing, and
whether subjective test items such as essay questions can be
automatically generated and graded.
• Besides, before the reliability and validity of these
automatically generated test questions are verified, it is
questionable whether such questions are suitable for highstakes tests.
3.5 Corpus-based interlanguage
analysis
• While Svartvik (1996), perhaps not without
hesitation, predicts that “corpus is becoming
mainstream”, Thomas & Short (1996), without any
hesitation, contend that “corpus has become
mainstream”.
• More encouragingly, Čermák (2003) says that “it
seems obvious now that the highest and justified
expectations in linguistics are closely tied to corpus
linguistics”, and that “it is hard to see a linguistic
discipline not being able to profit from a corpus one
way or another, both written and oral”.
• Teubert (2005) concludes that “Today, the
corpus is considered the default resource for
almost anyone working in linguistics. No
introspection can claim credence without
verification through real language data.
Corpus research has become a key element of
almost all language study.”
• Research on interlanguage is no exception. As
researchers in many linguistic disciplines are
enjoying the profits corpus linguistics
constantly brings, SLA researchers also come
to realize that most of the methodologies
(perhaps also some important notions about
what language is) in corpus linguistics also
have important light to shed on interlanguage
analysis.
• Needless to say, corpus-based interlanguage analysis requires
interlanguage corpora (or learner corpora) to be collected.
This is a relatively recent endeavor: it was not until the late
1980s and early 1990s that academics and publishers started
collecting learner corpora (Granger, 2002), and corpus-based
research on interlanguage began to be published when
Granger and her collaborators edited their collection of
papers in a volume called Learner English on Computer
(Granger, 1998).
• Not surprisingly, Granger’s efforts have been followed by
many SLA researchers the world over, and the learner corpus
has become “the default resource” for almost anyone
involved in interlanguage analysis.
• According to Granger (2002), corpus-based
interlanguage analysis usually involves one of
the two methodological approaches:
• Computer-aided Error Analysis (CEA) and
• Contrastive Interlanguage Analysis (CIA).
• Computer-aided Error Analysis
• Computer-aided Error Analysis (CEA) evolved from Error
Analysis (EA), which was a widespread methodology for
interlanguage analysis in the 1970s.
• EA has been criticized for several weaknesses:
• EA is based on heterogeneous data and therefore often impossible to
replicate;
• EA often suffers from fuzzy error categories;
• EA cannot cater for avoidance;
• EA only deals with what learners cannot do;
• EA cannot provide a dynamic picture of L2 learning.
• According to Dagneaux et al. (1999), these
weaknesses of EA highlight the need for a new
direction in EA analysis. Granger (2002:13)
forcefully contends that CEA is a great
improvement on EA in that the new approach
is computer-aided, involves a high degree of
standardization, and renders it possible to
study errors in their context, along with nonerroneous forms.
• The first way to conduct a computer-aided
error analysis is to search the learner corpus
for error-prone linguistic items using a
concordancer. The advantage is that it is very
fast and errors can be viewed in their context.
However, phenomena such as avoidance and
non-use remain a problem.
• The second way to conduct a computer-aided error analysis is more
common and powerful. This method requires a standardized error
typology to be developed beforehand.
• This typology is then converted to an error-tagging scheme, with
conventions about what label is to be used to flag each error type in the
learner corpus. Then, experts or native speakers are employed to read the
data carefully, and label each error in the corpus with its corresponding
tag in the error-tagging scheme. This process is very time-consuming, but
the efforts are rewarding. Once the tagging is complete, concordancers
can be used to search the corpus for each type of errors. The advantages
for this method are also obvious: error categorization is standardized; all
errors are counted and viewed in their context.
• Contrastive Interlanguage Analysis
• Contrastive Interlanguage Analysis (CIA) involves comparing a
learner corpus with another corpus or other corpora. Very
often, the purpose of such comparison is to shed light on
features of non-nativeness in learner language.
• Such features can be learner errors, but more often, they are
instances of overpresentation and underpresentation
(Granger, 2002). That is to say, by means of comparison, CIA
expects to find out which linguistic items in learner language
are misused, overused or underused. Besides, CIA also
attempts to discover the reasons for such deviations.
• According to Granger (1998; 2002), CIA involves two types of
comparison: NS/NNS comparisons and NNS/NNS comparisons.
NS/NNS comparisons are intended to reveal non-native
features in learner language. The assumption behind such
comparisons, as can be figured out, is that native speakers’
language can be regarded as a norm, to which the learners
are expected to come closer in the process of language
learning. The purpose of NNS/NNS comparisons is somewhat
different. Granger maintains that comparisons of learner data
from different L1 backgrounds may indicate whether features
in learner language are developmental (characteristic of all
learners at a certain stage of their language learning) or L1induced.
• In addition, Granger believes that bilingual corpora containing
learners’ L1 and the target language may also provide
evidence for possible L1 transfer. It is worth mentioning that
whichever comparison applies, a statistical test is often
necessary to reveal whether the difference found is
statistically significant. This methodology is drawing some
criticism. Some linguists insist that any variety of a language
has its own right to be a variety.
• The CIA methodology has yielded abundant research findings.
Most remarkably, Granger and her collaborators published
dozens of research articles, most of which are included in
Granger (1998) and Granger et al. (2002). Besides, the
empiricist methodology in the new era has also aroused the
interest of SLA researchers the world over, who have been
enthusiastically involved in the creation of new learner
corpora and corpus-based interlanguage studies, with the
result that an ever-growing number of such studies are
published in renowned international journals.
• In China, after the publishing of CLEC, two more learner
corpora, namely, COLSEC (Yang & Wei, 2005), and SWECCL
(Wen et al., 2005) have been released, providing data for
corpus-based interlanguage studies in the Chinese context. It
is particularly worth noting that SWECCL has two components,
SECCL (Spoken English Corpus of Chinese Learners) and
WECCL (Written English Corpus of Chinese Learners), with a
total of about 1 million words each.
• Undoubtedly, CIA is an effective methodology to reveal non-native
features in learner language. However, how to interpret non-native
features in learner language seems to be a more complicated matter,
which cannot be simply attributed to L1 induction or developmental
patterning.
• Factors such as input may also come into play. It may be necessary to
isolate sources of observed effects (cf. Juffs, 2001). For example, Tono
(2004) did an interesting study, in which she used a multifactorial analysis
to separate L1-induced variation from L2-induced variation and learner
input variation.
• Just as Cobb (2003) claims, learner corpus research ‘‘amounts to a new
paradigm, and a great deal of methodological pioneering remains to be
done’’.
4. Future trends
• Several trends now seem obvious in the use of
language corpora in applied linguistics:
1. With the growth of the power of computer hardware
and software and the increasing availability of more
texts, the sizes of general corpora may grow to
billions of words. While such large general corpora
may still be necessary for our understanding of
language, several types of specialized corpora may
be necessary in applied linguistics.
• For language learning and language teaching purposes, large
is not always beautiful. It is the usefulness of corpora that
really matters. After all, too much may not be better than
enough. For a pedagogic corpus, size and representativeness
are not as important as they are for a general corpus.
Neologisms, for example, may be interesting to researchers,
but they may not always be what we would like learners to
learn. It is very likely that corpora for learners to explore will
be carefully controlled in terms of the typicality and difficulty
of the language therein. This will probably also be the corpus
on which to base language testing, though not necessarily for
syllabus designing.
• For interlanguage researchers, representativeness of data will
always be an important consideration. However, for different
research purposes, what is going to be represented may also
be radically different. Therefore, various types of specialized
corpora may be necessary. Researchers may have to build
their own corpora to meet their specific needs. In corpusbased interlanguage analysis, native speaker norms will not
be put to death in at least a few decades’ time.
2. Corpus-based methods will have to be supplemented with
other methods (such as experimentation) if their findings are
to become more meaningful and convincing. Corpus
linguistics is virtually exclusively based on frequency (Gries, to
appear). “The assumption underlying basically all corpusbased analyses”, according to Gries, “is that formal differences
correspond to functional differences such that different
frequencies of (co-)occurrences of formal elements are
supposed to reflect functional regularities” (ibid: 5).
• The problem lies in that corpus methods exclusively rely on
searchable formal elements. The result is that it can be
difficult, if not altogether impossible, to infer the cognitive
process going on in the learners’ mind on the basis of what
can be found in text. In other words, corpus linguistics works
on the product. It does not help much when our interest is in
the process. Whenever necessary, other methodologies may
have to be assorted to as supplements.
3. With Construction Grammar of Cognitive Linguistics becoming
more widely accepted, interest in the role of phraseology will
keep growing. The idiom principle, which states that “a
language user has available to him or her a large number of
semi-preconstructed phrases that constitute single choices,
even though they might appear to be analyzable into
segments” (Sinclair 1991:110), will have an increasing effect
on language teaching, interlanguage analysis, and language
research in general.
4. Interlanguage analysis will focus more of its attention on
speech, and on syntactic aspects of learner language. These
aspects of interlanguage have by far not been adequately
explored. Besides, longitudinal studies may be especially
rewarding. Just as Cobb (2003:403) claims, it is “characteristic
of learner corpus methodology to extrapolate from crosssectional to longitudinal data” in order to address
developmental issues with respect to learner interlanguage.
Such data types are more likely to “accurately elucidate
developmental pathways”.
• Whether taken as a new discipline, a new theory, or a new
methodology, corpus linguistics is exerting influence on an
ever-growing number of people in applied linguistics. These
people might virtually be anyone in the applied linguistics
circles, from the syllabus designer to the materials writer,
from the language publisher to the dictionary user, from the
teacher to the learner, from the test maker to the test-taker,
and from the researcher to the researched.
• No matter how language corpora are used in applied
linguistics, for whatever purposes, the central theme of
corpus linguistics remains the same: what is more frequent is
also more important.
• References (omitted)
• The chapter is based on Liang Maocheng’s
Chapter in Wu Yi’an’s book (forthcoming).
Thank You!