Corpora in the classroom without scaring the students
Adam Kilgarriff
Lexical Computing Ltd
adam@lexmasterclass.com
1
Introduction
Corpora have a central role to play in our understanding of language. Over the last three decades we have
seen corpus-based approaches take off in many areas of linguistics. They are valuable for language learning
and teaching, as has been shown in relation to the preparation of learners' dictionaries and teaching
materials. Some language teachers have used them directly with students, but while there have been some
successes, 'corpora in the classroom' have not taken off as corpora in other areas of linguistics have. Most
attempts to use corpora in the classroom have been through showing learners concordances. The problem
with this is that most concordances are too difficult for most language learners - they are scared off.
However corpora can be used in the classroom in a number of other ways that are not based around (or do
not look like) concordances. In this paper, after a little history, we present two of them.
First of all, we say what a corpus is.
1.1
What is a corpus?
A corpus is a collection of texts. We call it a corpus when we use it for linguistic or literary research. An
approach to linguistics based on a corpus has blossomed since the advent of the computer, for three reasons:
 A computer can be used for finding, counting and displaying all instances of a word (or phrase or
other pattern). Before the computer, there were vast amounts of finding and counting to be done by
hand before you had the data for the research question
 As more and more people do more and more of their writing on computers, texts have started to be
available electronically, making corpus collection viable on a scale not previously imaginable. The
costs of corpus creation have fallen dramatically
 Computer programs to support the process have become available. Firstly, concordancers, which let
you see all examples, in context, of a search term, as in Fig 1.
added every year . I hope they don't run out of
space
. Of course, there was another 'music' recipient
holders who should ring 0870 606 2000 to book a
space
. Please remember that the area within 2 miles
couple stood isolated in an apparently infinite
space
created within an otherwise empty gallery.
community. News Member of Breakfast Club Plus?
Spaces
still available on FREE training course in Fife
add new ones ! So here goes then... . . The
Space
Dentists by Lovely Ivor Chapter 1 Drilly was
ruling party’s willingness to allow democratic
space
in the nation. Their response demonstrates
limited by the capacity of the available parking
spaces
. Parking spaces cannot normally be allocated
clear and pleasant way. It is designed to use
space
efficiently using �a white background�to complement
... ... ... ... ... . . Click here to visit The
Space
Dentists website Thank you for being one with
separate booklet . Answer the questions in the
space
provided below. In this internet paper the
for analysing and a vocabulary for appraising
spaces
and places. In wider educational terms it helps
disregarded and books were added where there was
space
on the shelves. The collection comprises c11,000
does not guarantee the availability of a parking
space
. A member of the University may register more
and uninteresting, leaving a large amount of
space
with out any information. It isn't until you
Tennessee accountant chris and now owns casino
space
more. Upcoming circuit buy-in enthusiasts from
London . Ring 0870 241 0541 to book and pay for a
space
, for directions, and for details of onward
fully as possible; if there is insufficient
space
in any of the boxes please continue on a separate
Then he heard some manic laughter, and the
space
bus's communications monitor flickered on in
like manner. This allows the viewer to probe a
space
that remains consistent for the duration of
Baudrillard explains that holographic" real
space
" provokes a perceptual inversion, it contradicts
Fig 1: A concordance. This is a sample of 20 lines for space in the UKWaC corpus, via the Sketch Engine.
Advanced concordancers allow a range of further features like looking for the search term in a
certain context, or a certain text type, and allow sorting and sampling of concordance lines and
statistical summaries of contexts. Also, tools like part-of-speech taggers have been developed for a
number of languages. If a corpus has been processed by these tools, we can make linguistic
searches for, for example, kind as an adjective, or treat followed by noun phrase and then adverb (as
in “she treated him well”)
2.
History
2.1
General
The history of corpus linguistics begins in Bible Studies. The bible has long been a text studied, in Christian
countries, like no other. Bible concordances date back to the middle ages.1 They were developed to support
the detailed study of how particular words were used in the Bible.
After Bible Studies came literary criticism. For studying, for example, the work of Jane Austen, it is
convenient to have all of her writings available and organised so that all occurrences of a word or phrase can
be located quickly. The first Shakespeare concordance dates back 200 years. Concordancing and computers
go together well, and the Association for Literary and Linguistic Computing has been a forum for work of
this kind since 1973.
Data-rich methods also have a long history in dictionary-making, or lexicography. Samuel Johnson used
what we would now call a corpus to provide citation evidence for his dictionary in the eighteenth century,
and the Oxford English Dictionary gathered over twenty million citations, each written on an index card
with the target word underlined, between 1860 and 1927.
Psychologists exploring language production, understanding, and acquisition were interested in word
frequency, so a word’s frequency could be related to the speed with which it is understood or learned.
Educationalists were interested too, as it could guide the curriculum for learning to read and similar. To
these ends, Thorndike and Lorge prepared ‘The Teacher’s WordBook of 30,000 words’ in 1944 by counting
words in a corpus, and this was a reference set used for many studies for many years. It made its way into
English Language Teaching via West’s General Service List (1953) which was a key resource for choosing
which words to use in the ELT curriculum until the British National Corpus (see below) replaced it in the
1990s.
1
See http://www.newadvent.org/cathen/04195a.htm for a summary.
Following on from Thorndike and Lorge, in the 1960s Kučera and Francis developed the landmark Brown
Corpus, a carefully compiled selection of current American English of a million words drawn from a wide
variety of sources. They undertook a number of analyses of it, touching on linguistics, psychology,
statistics, and sociology. The corpus has been very widely used in all of these fields. The Brown Corpus is
the first modern English-language corpus, and a useful reference as a starting-point for the sub-discipline of
corpus linguistics, from an English-language perspective.
While the Brown Corpus was being prepared in the USA, in London the Survey of English Usage was under
way, collecting and transcribing conversations as well as gathering written material. It was used in the
research for the Quirk et al Grammar of Contemporary English (1972), and was eventually published in the
1980s as the London-Lund Corpus, an early example of a spoken corpus.
2.1.2
Theoretical linguistics
In 1950s America, empiricism was in vogue. In psychology and linguistics, the leading thinkers were
advocating scientific progress based on collection and analysis of large datasets. It was within this
intellectual environment that Kučera and Francis developed the Brown Corpus.
But in linguistics, Chomsky was to change the fashion radically. In Syntactic Structures (1957) and Aspects
of the Theory of Syntax (1965) he argued that the proper topic of linguistics was the human faculty that
allowed any person to learn the language of the community they grew up in. They acquired language
competence, which only indirectly controlled language performance. ‘Competence’ is a speaker’s internal
knowledge of the language, ‘performance’ is what is actually said, and the two diverge for a number of
reasons. To study competence, he argued, we do better to make native-speaker judgements of what
sentences are grammatical and which are not, rather than looking in corpora where we find only
performance.
He won the argument - at least for a few decades. For thirty years, corpus methods in linguistics were out of
fashion. Many of the energies of corpus advocates in linguistics have been devoted to countering
Chomsky’s arguments. To this day, in many theoretical linguistics circles, particularly in the USA, corpora
are viewed with scepticism.
2.1.3 Lexicography
Whatever the theoretical arguments, applied activities like dictionary-making needed corpora, and in the
1970s it became evident that the computer had the potential to make the use of corpora in lexicography
much more systematic and objective. This led to the Collins Birmingham University International Language
Database or COBUILD, a joint project between the publishers and the university to develop a corpus and to
write a dictionary based on it. The COBUILD dictionary, for learners of English, was published in 1987.
The project was highly innovative: lexicographers could see the contexts in which a word was commonly
found, and so could objectively determine a word’s behaviour, as never before. It became apparent that this
was a very good way to write dictionary entries and the project marked the beginning of a new era in
lexicography.
At that point Collins were leading the way: they had the biggest corpora. The other publishers wanted
corpora too. Three of them joined with a consortium of UK Universities to prepare the British National
Corpus, a resource of a staggering (for the time, 1994) 100 million words, carefully sampled and widely
available for research.2
2.1.4 Computational Linguistics and Language Technology
The computational linguistics community includes both people wanting to study language with the help of
computer models, and people wanting to process language to build useful tools, for grammar-checking,
question-answering, automatic translation, and other practical purposes. The field began to emerge from
2
http://www.natcorp.ox.ac.uk/
Chomsky’s spell in the late 1980s, and has since been at the forefront of corpus development and use.
Typically, computational linguists not only want corpora for their research, but also have skills for creating
them and annotating them (for example, with part-of-speech labels: noun, verb etc) in ways that are useful
for all corpus users. In 1992 language technology researchers set up the Linguistic Data Consortium, for
creating, collecting and distributing language resources including corpora.3 It has since been a lead player in
the area, developing and distributing corpora such as the Gigaword (billion-word) corpora for Arabic,
Chinese and English. Now, most language technology uses corpora, and corpus creation and development is
also a common activity.
2.1.5 Web as Corpus
In the last ten years it has become clear that the internet can be used as a corpus, and as a source of texts to
build a corpus from. The search engines can be viewed as concordancers, finding examples of search terms
in ‘the corpus’ (e.g., the web) and showing them, with a little context, to the user. A tool called BootCaT
(Baroni and Bernardini 2004) makes it easy to build ‘instant domain corpora’ from the web. Some large
corpora have been developed from the web (e.g., UKWaC, see Ferraresi et al 2008). Summaries and
discussions of work in this area and the pros and cons of different strategies are presented in Kilgarriff and
Grefenstette (2003) and Kilgarriff (2007).
2.2
History of Corpora in ELT
As noted above, corpora have had a role in ELT for many years, in the selection of vocabulary to be taught.
Since COBUILD, the role has moved to the foreground, from a theoretical as well as a practical point of
view. John Sinclair, Professor at Birmingham University and leader of the COBUILD project, argued
extensively that descriptions of the language which are not based on corpora are often wrong and that we
shall only get a good picture of how a language works if we look at what we find in a corpus. His
introductions to corpus use, Corpus, Concordance, Collocation (1997) and Reading Concordances (2003)
give many thought-provoking examples. Sinclair’s approach has inspired many people in the languageteaching world.
In parallel developments within the communicative approach to language teaching, authors including
Michael Lewis (1993), Michael McCarthy (1990) and Paul Nation (1991) have made the case for the central
role of vocabulary. This fits well with a corpus-based approach: whereas grammar can be taught ‘top
down’, as a system of rules, vocabulary is better learnt ‘bottom up’ from lots of examples and repetition, as
found in a corpus. Corpora (and word frequency lists derived from them) also provide a systematic way of
deciding what vocabulary to teach.
In the years since COBUILD, all ELT dictionaries have come to be corpus-based. As they have vast global
markets and can make large sums of money for their publishers, competition has been fierce. There has been
a drive for better corpora, better tools, and a fuller understanding of how to use them. Textbook authors
have also taken on lessons from corpora, and many textbook series are now ‘corpus-based’ or ‘corpusinformed’.
2.2.1 Learner Corpora
A corpus of language written or spoken by learners of English is an interesting object of study. It allows us
to quantify the different kinds of mistakes that learners make and can teach us how a learner’s model of the
target language develops as they progress. It will let us explore how the English of learners with different
mother tongues varies, and the kinds of interventions that will help learners. Several corpora of this kind
have been developed.
2.3
In the ELT classroom
Corpus use in ELT can be classified as ‘indirect’ or ‘direct’. Indirect uses are, as described above,
developing better dictionaries and teaching materials (and also testing materials, not discussed here). Direct
methods are bolder: in these, we aim to use the corpus with the learners in the classroom.
3
http://ldc.upenn.edu
The pioneer was Tim Johns, also working at Birmingham University, mainly with graduate and
undergraduate students (see, e.g., Johns 1991). In the 1980s, on very early computers, he developed
concordancing programs and strategies for using the concordances in the classroom: he coined the term
‘Data-Driven learning’.4 The arguments he presented for it were, and remain, that it shows the learner real
language, that it encourages them to test hypotheses about how words and phrases are used, and language
facts learnt in this way are very likely to be remembered.
Concordances can operate as ‘condensed reading’ (Gabrielatos 2005). Various authors have made the case
that the richest and most robust vocabulary learning come from extensive reading. But how should this be
focused? How can we know whether enough vocabulary, or, the right vocabulary, will be covered, and will
vocabulary items be encountered only very occasionally, so will not be reinforced? And what classroom
exercises will support it? One response to these questions is to support extensive reading with exercises
looking at concordances for the target vocabulary. In this way, the learners’ attention will be focused on the
item, they will see many examples of it, and they can observe its meaning(s) and notice the patterns in which
it occurs. Cobb (1999) presents a clear picture of how this can be implemented: he treats his students as
lexicographers who need to establish the meaning and use of 200 previously-unknown (to them) words per
week. This they do by looking at concordances. His experiments show marked improvement over other
methods in learning and retention, and offer prospects of his first-year undergraduate students acquiring the
2500 words that, he estimates, they need to learn in their one-year course.
Boulton (2009) takes a more focused approach, looking at linking adverbials, an area where learners clearly
have difficulty. In his experiments, again with first-year undergraduates, he compared learning by one set of
students using dictionaries with another using corpora, and found significantly better recall in the corpus
group. His choice of focus is indicative of the area where ‘concordances in the classroom’ have most to
offer: in relation to common expressions, often multi-word, which students will often want to use in their
own speaking and writing, and where the challenge is not presented by the meaning so much as the patterns
of use. For straightforward matters of meaning, the dictionary is a direct route to the goal, and good modern
dictionaries will often also give a substantial amount of information about the word’s various meanings,
collocations and patterns of use. But the information is often concise and abstract, to fit within the
limitations of a dictionary entry, and often does not cover the particular situation in which the learner plans
to use the word or phrase: it does not give a relevant example or collocation. The corpus is a place to go
when the dictionary does not tell you enough.
Taiwan has been a leading location for work in this area, with, among others, Yeh, Liou and Li (2007) using
concordances to support the use of a variety of near-synonymous adjectives, rather than always using the
most common; Sun and Wang (2003) exploring collocation-learning with high school students, and Chan
and Liou (2005), with undergraduates; and Sun (2007) using a concordance-based writing tool with her
students. Tim Johns also worked on a project in Taiwan shortly before his death, as described in Johns, Lee
and Wang (2008).
The ‘Teaching and Language Corpora’ (TALC) community held its first conference in 1994 and one has
been held every two years since. Papers at the conference typically describe the lecturer’s use of corpora in
their teaching.
3
Why are there not more corpora in classrooms?
‘Corpora in the classroom’ have been on the ELT agenda for twenty years but remain a specialist niche.
People who attend TALC conferences are largely language teachers whose research interests are in corpus
linguistics, so they are aiming to bring their research into their teaching. They are not language teachers
who simply want more tools for their repertoire. Most English language teachers have not used them, and
Some brief and stimulating examples of Johns’ style of work can be seen at
http://www.eisu2.bham.ac.uk/johnstf/timeap3.htm#revision . Sadly, Tim Johns died in 2009.
4
may not have even heard of them. This is probably true at the university level and certainly true at the high
school level.
Why is this? Firstly, I think, because at first glance they do not meet language learners’ needs. To find out
what a word means I can look it up in a dictionary or infer its meaning from the context in which I
encountered it. Looking it up in a corpus presents lots of work, lots of distractions, and I might not find the
answer I want in the end.
Second, it does not address motivation. Central to the language teacher’s task is motivating the students.
Presenting them with concordance lines – typically a page of fragments of dense text - is not promising.
TALC practitioners’ answer to this is that, for advanced students, the motivation comes from the hunt to
work out the rules and generalisations governing the vocabulary item. For some academically-inclined
students, this is good. But most students do not want to learn how to do corpus linguistics, they want to
learn English. The excitement and motivation about the method often relates to the teacher not the students.
Third, it is not clear that it works. Most evaluations have been small-scale and situation-specific, and have
not had resounding results.
Finally, and, to my mind, underlying all the points above, concordances are hard to read. Whether they are
fragments of sentences or full sentences, they come without a discourse context and they are likely to throw
at the learner a whole array of complexities and difficulties. In my experience as a native speaker, reading
concordances is a specialist skill that has taken a while to learn. In particular, it works well if you can very
quickly understand the gist of the corpus line, so you know immediately the meaning, in this context, of the
target word or phrase. Making this assessment depends on large numbers of clues embedded in collocations
and syntactic patterns, usually within five words either side of the target. Often, culture-specific inferences
need to be made to understand the sentence, making matters very difficult for learners of the language. In
order to learn anything about word meaning from the corpus line, it is first necessary to decode the line
itself: for learners, this is no trivial task.
When reading concordances, it is better not to spend too much time on one line. The corpus should be
looked at for what is apparent from repeated occurrences, and facts about the concordance line that are not
immediately apparent will not contribute. Unless you work through a batch of concordances at some speed,
the process will tend to be too drawn out for the patterns to reveal themselves. One part of the skill of
reading concordances is knowing which lines to ignore, perhaps because the word is being used in an
unusual or humorous way, or is not in a sentence at all. It is a judgement I (as an experienced corpus user
and native English speaker) can make in a second or less. For many learners, simply getting the gist of each
line will take a while and to expect them to work out which lines to ignore is unreasonable.
4
Between dictionary and corpus
Learners’ dictionaries are designed to help learners understand and use words and phrases. A corpus is
another resource to help with the same task. How do they relate to each other?
They are both records of the language. The corpus is a sample of the language in the raw. The dictionary is
a highly condensed version of roughly the same material. The relation between the two is easy to see when
we consider how modern corpus-based dictionaries are prepared. One of the main inputs, at leading
dictionary publishers including Collins, Macmillan and Oxford University Press, is word sketches: one-page
corpus-based summaries of a word’s grammatical and collocational behaviour, as in Fig 2.5 Is this more
corpus-like or dictionary-like? It is automatically-produced output from the corpus, making it corpus-like,
but it is a condensed summary of what was found there, making it dictionary-like. On a continuum from
corpus to dictionary, it is somewhere in the middle.
5
Word sketches are one of the functions in the Sketch Engine (Kilgarriff et al 2004), a leading corpus query tool in use
at a number of dictionary publishers and universities worldwide. A free trial is available at http://sketchengine.co.uk .
Most learners do not want to be corpus linguists, and concordances are unfamiliar and difficult objects. But
dictionaries are familiar from an early age, sometimes even loved. Learners will not be put off if they are
expected to look items up in a new kind of dictionary. This suggests a strategy for bringing corpora into the
classroom: disguise them as dictionaries.
Dictionary-users often find the examples are the most useful part of a dictionary entry. Moreover, where
dictionaries are electronic rather than on paper, the traditional space limitation on examples disappears: there
is room for lots of examples. This is an area where the corpus can help: they are nothing but examples.
However they are not selected or edited examples. Choosing examples for a dictionary is an advanced
lexicographical skill: they should be short, use familiar words, without irrelevant grammatical complexity,
and they should give a typical example of the word in use and provide a context which helps the learner
understand what it means.
While we cannot yet program computers to do the task anything like as well as people, we can perform some
parts of it automatically. We can rule out sentences which are too long, or too short, or which contain
obscure words, or which have many words capitalised or lots of numbers or square brackets or other
characters which are rare in the kind of simple, straightforward sentences we are looking for. We have done
that in a program called GDEX (Good Dictionary Example finder, Kilgarriff et al 2008). The initial project
was a collaboration with Macmillan, and the examples were used as a first-pass filter for them to add more
examples to their dictionary. The machinery has been embedded into the Sketch Engine, and concordance
lines can now be sorted according to GDEX score, so the ‘best’ ones are the ones that the user sees as their
search hits.
space
ukWaC freq = 273022
object_of 86021 2.2
pp_between-i 2685 10.0 pp_above-i 205 5.9
watch
4113 9.21 paragraph
44 4.46 shop
confine
1200 8.65 particle
22 3.64
occupy
1230 8.22 row
24 3.5 pp_per-i
allocate
912 7.95 column
limit
1163 7.76 word
fill
1179 7.68 tooth
enclose
566 7.41 building
410
143 3.1 dwelling
4.7
63 1.83
city
28 1.26
area
58 0.18
16 9.55 centre
19 0.14
44 5.48
17 3.09 unit
18 0.57 pp_down-i 76 4.2
83 2.22 person
26 0.57 left
3038 7.39 seat
20 2.2
save
1097 7.24 letter
39 2.1
reserve
523 7.08 star
20 2.07
devote
381 6.8 wall
32 1.97
breathe
344 6.79 cell
29 1.73
pp_for-i
15742 4.1
17 2.75
right
29 0.51
pp_around-i 515 4.6
building
a_modifier 106533 2.7
109 6.31 open
29 1.73 building
25 3.2 sq.m
create
wheelchair
pp_within-i 983 4.2
19 0.1
n_modifier 64957 2.6
10693 9.69 parking
adj_subject_of 8065 2.3
6052 10.16 available
3224 6.63
fridge/freezer
40 6.13 green
3787 9.18 disk
2762 9.43 adjacent
91 6.37
freezer
49 5.93 outer
1732 8.75 storage
3084 9.32 cramped
20 5.79
fridge
52 5.62 public
5574 8.18 exhibition
1689 8.08 tight
70 5.76
cot
35 5.56 empty
1268 8.09 office
2984 7.75 limited
130 5.45
contemplation
27 5.43 ample
488 7.59 finite
17 4.83
970 8.05 breathing
reflection
91 5.42 enough
1564 7.78 loft
426 7.57 scarce
16 4.8
recreation
47 5.4 limited
1110 7.54 gallery
872
41 4.64
luggage
35 5.16 extra
1325 7.46 roof
812 7.47 accessible
69 4.59
update
108 5.01 urban
959 7.45 floor
1535 7.41 inadequate
20 4.41
dryer
24 4.91 short
2051 7.37 living
798 7.39 suitable
91 4.41
storage
99 4.9 much
1740 7.21 studio
755 7.37 efficient
44 4.08
7.5 empty
Fig 2. Word sketch for space drawn from UKWaC (truncated)
While GDEX could be much better, it already makes it more likely that corpus examples will be readable.
5
Corpora in the classroom without concordances
Concordance tools typically give the user lots of options as to how the search is specified and displayed,
whereas dictionaries provide less: the lexicographers have decided what the learner should know about the
word, so there is no need to complicate the learner’s experience by giving lots of options. So when
disguising a corpus as a dictionary, the designer should make choices and not leave them to the user.
We can use the word sketch machinery for finding grammatical and collocational patterns, and GDEX for
selecting examples for those patterns. That then gives us a fully automatic collocations dictionary. The
entry for space in this dictionary is shown in Fig 3. 6
space (n)
object_of
watch :
We are also hoping to hold another Dinner in the Autumn – watch this space !
confine :
Out of the bag the process will be quicker , but badges in confined spaces should last
better for these reasons .
occupy :
A separate music library was formed in 1975 utilising space formerly occupied by
committee rooms .
allocate :
Externally there is a single garage which is situated en bloc plus an allocated parking
space .
fill :
Food food safe filler is used to fill any spaces in the container .
enclose :
Where a temple is found within an enclosed space , this is in most cases a rectangular
space aligned with the temple .
a_modifier open :
6
Open Spaces There will be a clean up day in November .
Available at http://www.forbetterenglish.com
modifies
green :
The Green Flag Award scheme is the national standard for parks and green spaces .
outer :
Moreover , we shall not be the first to place any weapons in outer space .
ample :
The bright reception room is a generous size , allowing ample space for a dining table .
empty :
Void size is a measure of how much of the medium consists of empty space .
public :
Streets are well overlooked helping to make public spaces feel safe .
shuttle :
Fly my space shuttle into the sun on my 105th birthday .
exploration:
Why am I so concerned about Britain 's role in space exploration ?
n_modifier parking :
6
However , there would be a lot of scope for disputes where the number of
physical parking spaces exceeded the number in the licence .
disk :
We do not set any limit on how much disk space is used .
storage :
There was no storage space for his personal possessions .
exhibition :
Recently lottery approval was received to completely restructure the existing building to
provide considerably increased exhibition space built to the highest standards of design .
office :
The removal of interior partitions will also allow new office space to be created .
living :
Building and decorating companies are available now to help you to create the
ideal living space for you .
Corpora that motivate the students
Wouldn’t it be nice if each student could work on English texts about a topic they were genuinely interested
in? Rather than reading about safe textbook subjects like the family, or holiday traditions, or Harry Potter,
they could find and learn from texts on gaming, hip hop, manga, or whatever they find fun. That would go a
long way to addressing motivation.
We have a tool that allows students to collect corpora on a topic of their choice: it is called WebBootCaT
(Baroni et al 2006) and it uses the vast resource of the web. The user inputs five or six ‘seed terms’ in the
domain of interest. Triples of seed terms are then sent to the Yahoo search engine, and Yahoo returns with a
page of search hits; the program then gathers these pages and builds a corpus of them. A corpus of 300,000
words typically takes a few minutes, though, if the corpus is to be used extensively, further rounds of
examining and improving the corpus are recommended (and supported by the software).
Smith (2009) offered his Taiwanese first-year undergraduates a project in which they used WebBootCaT to
build their own corpus. If the students are sufficiently engaged in the topic, they won’t be scared off.
7
Summary
In this paper I have given some background on what corpora are and how they have been used in a variety of
fields, focusing on ELT. Corpus use has become standard in dictionary and textbook preparation, but using
them directly with students remains a specialist activity. This is explained as following mainly from the
difficulty that learners have in reading concordances. I present an alternative strategy, in which corpora do
come into the classroom – to help students where the dictionary does not tell them enough – but presented as
dictionaries. Automatic techniques allow us to do this well: a word sketch is midway between corpus and
dictionary We show how this can be done with an automatic collocations ‘dictionary’.
We also present a response to the question of motivation: we provide technology for students to build their
own corpora. I’ll leave the final words with one of Smith’s (2009) students who took the project:
I find it special to have your own corpus. It is unique! You can make corpuses by your interests.
That can make you know words easily because words are about your own interests.
References
Baroni, M. and S. Bernardini. 2004. BootCaT: Bootstrapping corpora and terms from the web. Proceedings
of LREC, Lisbon: ELDA. 1313-1316.
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