Introduction to Corpus Linguistics
1. What is a corpus?
 Latin corpus, “body”, plural corpora
 A body of text - a collection of texts – a large database of texts/linguistic examples
Characteristics of a corpus according to McEnery and Wilson (2001)
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Machine-readable form – saved on and analysed by a computer
Usually very large – but of a finite size
Representative sample – Corpora are so big so that they can be a “representative sample” of a
language
o large enough to establish norms about the form of language being studied
o large enough to reveal lots of cases of rare or unusual uses of language that we might
not get from a smaller sample.
o standard reference or ‘benchmark’
Often annotated – extra linguistic information added to the raw data
o Annotation also known as TAGGING
o e.g. the grammatical class of each word in a text (noun, verb, etc)
A corpus could be described as “a finite-sized body of machine-readable text, sampled in order to be
maximally representative of the language variety under consideration.” (p.32)
2. Corpus Linguistics
Characteristics of corpus linguistics according to Biber, Conrad & Reppen (1998:4)
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Uses a corpus
Uses computers for analysis
Empirical – analysing actual patterns of language use
Depends on quantitative and qualitative analytical techniques
 Corpus linguistics is the study of language using corpora
 The basic idea: by analysing VERY large amounts of textual data, we can find out things
about language that we can’t find out any other way
 Allows us to test theories about language
 Removes bias
 Helps us to spot common and rare language phenomena
Corpora may contain different sorts of data…
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Written data – extracts from books, magazines, newspapers, websites…
Spoken data – transcripts of meetings, lectures, radio programs, everyday conversations…
Some corpora contain a mixture of both (so both sorts of language are represented)
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3. Empiricism and the history of corpus linguistics
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Corpus linguistics is an empirical method – it based on real-world data
However, at the time when the first very large corpora were being compiled, empirical methods
were out of fashion in linguistics.
This was due to the Chomskyan revolution in linguistics, in which methods based on
introspection and intuition were much more important.
Empirical approaches pay less attention to the internal mind of an individual native speaker,
and recognise that people’s intuitions about their own language may not be accurate
So the only way to see how language is used, is to look at examples of ordinary, everyday
language (written or spoken)
However, after a period of some years the tide turned and corpora began to be used more widely. Some
important milestones include:
1960s
The Brown corpus
1 million word sample of American English Compiled by Francis and
Kucera, Brown University. Classed as “a waste of time” by some of their contemporaries
http://icame.uib.no/brown/bcm.html
1970s
The LOB corpus “Lancaster-Oslo/Bergen”. British English match for Brown
Compiled by Geoffrey Leech at Lancaster and others
http://clu.uni.no/icame/manuals/BROWN/INDEX.HTM
1980
The London-Lund corpus
500K words of spoken British English
http://clu.uni.no/icame/manuals/LONDLUND/INDEX.HTM
1986
The Kolhapur Corpus (and other international corpora) 1 million words of Indian English
http://clu.uni.no/icame/manuals/KOLHAPUR/INDEX.HTM
1980s
1996: The Helsinki Corpus
Texts from 750CE to 1700CE
Diachronic corpus (includes text across time). 1.5 million words.
http://clu.uni.no/icame/manuals/HC/INDEX.HTM
Later corpora have tended to get bigger and bigger:
1990s
The British National corpus
100 million words of British English. (produced by a consortium
including Lancaster)
http://www.natcorp.ox.ac.uk/
The Bank of English
524 million words (and counting)
http://www.titania.bham.ac.uk/
2000s
The English Gigaword
1756 million words, released 2003, consists mostly of newswire
data http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2003T05
However, corpus linguistics also uses smaller, more specialised corpora, for example:
Lancaster Newsbooks Corpus (Tony McEnery and Andrew Hardie). 800,000 words of text from newspapers
published in the first half of 1654. Used to study the origins of English journalism
http://www.lancs.ac.uk/fass/projects/newsbooks/
Note that the lists above are just a sample of a few of the very many corpora that have been collected
in a huge range of languages and dialects. See URLs below for a few more examples:
http://clu.uni.no/icame/manuals/
http://ucrel.lancs.ac.uk/corpora.html
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4. The British National Corpus (BNC)
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Currently, one of the best contemporary UK English corpora
100 million words
from the early 1990s
represents a wide range of both spoken and written modern British English
Written data
o 90 million words
o Includes extracts from newspapers, academic books, popular fiction, letters and university
essays
Spoken data
o 10 million words
o Includes demographic data and context governed data
The demographic part
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transcripts of about 900 everyday unscripted spoken conversations
The context-governed part
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spoken language collected in public contexts – e.g. radio phone-ins, government
meetings, classroom interactions
5. Exploiting a corpus
5.1 Wordlists (also known as frequency lists)
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All of the words in a corpus presented in alphabetical order or sorted according to their
frequency.
Basic word counts: how often does each word occur in the corpus?
frequency lists compiled by computer
This can give us more useful information than you might suspect…
… but the frequency statistics still needs interpreting
5.2 Concordances
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A computerised search for all the examples of a word or a phrase in a corpus.
Concordance lines show the results of the search and include the search term plus some of the
surrounding context for each example.
Usually shown in a sort of table with the search term highlighted in some way
This informs us about the ways in which a word is usually used in actual sentences
5.3 Collocation
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Collocation is the relationship between words that tend to occur together
Words which occur more frequently in the vicinity of word X than they do elsewhere in the
corpus are called the collocates of X.
Based on frequency (how frequent separate vs. how frequent together)
Some computer tools can discover non-obvious collocates using statistics.
Collocation forms part of a word’s meaning
o “You shall know a word by the company it keeps” (Firth 1957:11)
o The company a word keeps: implicit associations or assumptions
o Near-synonyms often differ in terms of their collocations…
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5.4 Statistical significance
Sometimes just looking at frequency in a text can be a bit useless…
 We are really interested in words or other linguistic features that are unusually prominent … that
is, more frequent in the text than in the language as a whole
 These are often the features that have most importance for a text
 But how do we know what counts as “unusually prominent”?
 We can use mathematical procedures called significance tests
 We can work out whether a difference in the statistics is significant or not
 A test often used in humanities is called the Chi-square test, but in corpus linguistics we often use
one called log-likelihood
5.5 Keywords
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A keyword is a word that is unusually frequent in a text or corpus
Based on comparison with a benchmark or reference corpus
The difference must be statistically significant
Keywords are often important to the meaning or theme(s) of the text
They can be a good guide to what would be interesting to investigate further
6. Corpus annotation
Annotation = extra information added to the text ( also known as TAGGING)
EG: the grammatical class of each word in a text (noun, verb, etc) (POS tagging – see below)
6.1 Part-of-speech (POS) tagging
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POS tagging is the most basic type of grammatical analysis of a corpus.
An example tagset: the C7 tagset (devised at Lancaster). See:
http://ucrel.lancs.ac.uk/claws7tags.html
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For example – verbs
o V = all verbs
o VV, VB, VH, VD = lexical and auxiliary verbs
o VV0 = root form “go”, “see”
o VVZ = -es form “goes”, “sees”
o VVG = -ing form “going”, “seeing”
o VVD = past tense “went”, “saw”
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Tags are useful for improved corpus searches
For instance, can
o can as Verb – ‘He can do it’
o can as Noun – ‘That’s opened a can of worms!’
So, we can use tags to narrow the search
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Tagging is usually done not by hand but by a specialised computer program. An example POS tagger
program: the CLAWS system (developed at Lancaster). See:
http://ucrel.lancs.ac.uk/claws/trial.html
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6.2 How do taggers work?
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Some words are looked up in a computerised dictionary or lexicon
o Other words can have multiple tags: e.g. “dogs” – plural noun (NN2) or third person singular
verb (VVZ)?
o Words like this are worked out based on the surrounding tags by 1 of 2 methods:
 Rule-based – the computer is given rules about what tags can and can’t possibly
follow one another and uses these to work out the correct tag
 Probabilistic (or stochastic) – the computer uses information about how frequently
tags follow one another to work out what the tag is likely to be
Tagger programs make mistakes: they are not 100% accurate (CLAWS 96-97% accurate)
Note that, particularly in probabilistic tagging, the computer extracts information from a corpus
and uses that information to help it understand / analyse / process other examples of language.
6.3 Semantic tagging
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Computers can, to some extent, understand the meaning of words in the same way they
understand grammar
Each word is given a label to say what type of meaning that word has
As with POS tagging, semantic tags can be added to text by a computer program
At Lancaster we use 21 basic semantic categories. See: http://ucrel.lancs.ac.uk/usas/
These have more detailed subcategories. See http://ucrel.lancs.ac.uk/usas/semtags.txt
6.4 Why annotate?
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Training the computer to process language better
Helping searches in corpora
Allowing us to search for / count up patterns of grammar or meaning, rather than just the
words
There can be an “upward spiral” effect: a machine-tagged corpus can be checked, improved, then fed
back in again to produce an even better tagging program!
7. Applications of Corpora and Corpus Linguistics
7.1 Corpora in lexicography
 Early lexicography: the lexicographer’s brain – Samuel Johnson’s dictionary
 Later lexicography: readers’ observations – The Oxford English Dictionary (1st ed.)
 Modern lexicography is based largely on corpora
7.2 Other applications
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The study of grammar
Variation studies
Historical studies
Comparing different languages
Stylistics research
 And there are “real-world” applications
EG: machine translation requires both Dictionary lookup (corpus-based lexicon) and
Grammar analysis for good translation
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8. Conclusion
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Corpus linguistics is an empirical methodology
It relies on scanning large computerised bodies of text
Corpus methods were frowned upon during the “Chomskyan revolution” in the 1960s…
… but in the 70s, 80s and 90s they have come into their own
There are different ways to get information from a corpus: frequency lists, concordances, etc.
Corpora can be used in, for example, lexicography, the study of grammar, etc.
Computers can be programmed to process language to some degree – often by absorbing linguistic
knowledge from annotated corpora
 The computer can’t do it all for us – we still have to analyse the results and ask “but what does all this
mean??”
References
Biber, D. Conrad, S. & Reppen, R. (1998) Corpus Linguistics: Investigating Language Structure and Use.
Cambridge University Press.
Firth, J.R. (1957) A synopsis of linguistic theory, 1930–1955. Studies in Linguistic Analysis
(pp. 1–32). Special Volume, Philological Society.
McEnery, T. & Wilson, A. (2001) Corpus Linguistics (2nd Ed.). Edinburgh University Press.
Further Reading
Aijmer, K. (1991) English Corpus Linguistics. London: Longman.
Archer, D (2007) Computer-assisted literary stylistics: the state of the field. In Lambrou, M and Stockwell, P (eds.)
Contemporary Stylistics. London: Continuum. Pages 244-256.
Baker, P, Hardie A and McEnery, A (2006) A glossary of Corpus Linguistics. Edinburgh: Edinburgh University
Press.
Hunston, S (2002) Corpora in applied linguistics. Cambridge: Cambridge University Press.
Kennedy, G. (1998) An Introduction to Corpus Linguistics. London: Longman.
Mahlberg, M. (2007) ‘A corpus stylistic perspective on Dickens's Great Expectactions’. In M. Lambrou and P.
Stockwell (eds) Contemporary Stylistics. Routledge, pp. 19-31.
McEnery, A., Z. Xiao & Y. Tono (2005) Corpus-based language studies : An advanced resource book . London :
Routledge.
Meyer, C. (2002) English Corpus Linguistics. Cambridge University Press.
Semino, E and Short, M (2004) Corpus stylistics : speech, writing and thought presentation in a corpus of English
writing. London: Routledge.
Tribble, C. (2000) Genres, keywords, teaching: towards a pedagogic account of the language of project
proposals. In Burnard, L. and McEnery, T. (eds) Rethinking language pedagogy from a corpus perspective. Berlin:
Peter Lang, pp 74-90
Some Internet Resources
Short course on corpus linguistics on the Lancaster
University website
The research centre for Lancaster University
BNC Frequencies
BNC query tool
Log Likelihood information/calculator
http://www.lancs.ac.uk/fss/courses/ling/corpus/
http://ucrel.lancs.ac.uk/
http://ucrel.lancs.ac.uk/bncfreq/
http://corpus.byu.edu/bnc
http://ucrel.lancs.ac.uk/llwizard.html
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