Harnessing keyness: Corpus-based
approach to ESP material development John BlakeBackground
ESP course developers adopting a corpus-based or corpus-driven approach1 cancreate a focus corpus of relevant texts and use keyness2 to identify lexical sets tointegrate into teaching materials.
Keyness• Keyness is a measure of the frequency of disproportionate occurrence.
• Focus corpus is contrasted to a reference corpus.
• Operational definition of keyness3 affects key words.
• Words may be key to whole corpus (global) or part (localised or bursty)4.

Table 1: Focus corpus
VariableCount
Tokens2,516,051
Words
1,966,650
Sentences
77,547This study investigates the effect of different 1. concordancers, 2. reference
corpora and 3. statistical formulae on the keyword lists for a focus corpus ofresearch articles on international business.

1: Concordancer
Concordancers are designed for different
purposes and budgets. Raw frequency
counts also differ with concordancer5. 4thgeneration concordancers6 are webbased, fast and suitable for large corpora.
This presentation compares the results
using two popular concordancers: AntConc
and Sketch Engine.

Fig 1:
Sceenshotof AntConc

Table 2: Examples of 3G &4G concordancers
3G AntConc7UAM Corpus Tool8
Wordsmith Tools9
4G CQPweb10
Sketch Engine11
Wmatrix12

List 1

2: Reference corpus
The genre & diachrony of a corpus
significantly affect keyness13, 14.
AntConc users need to provide theirown reference corpus, while Sketch
Engine provides access to 20 corpora,including enTenTen12, a 12 billion
token corpus.

Many linguists are ill at ease with statistics15.
Main ideas• Frequency bias of log likelihood and chi
squared3• Rarity bias of mutual information• The simple maths version16 in Sketch
Engine names the formulae clearly andassumes language is not random17.

Summary
4G concordancers withthe choice of reference
corpora and formulae
enable developers to
tweak results to create
the most useful list ofkeywords.

Keyword list
AntConc
Brown
Log likelihood
1 the
2 firms
3 firm
4 al
5 et
6 in
7 knowledge
8 market
9 this
10 table

List 5

Keyword list
AntConc
Brown
Chi squared
1 the2 firms
3 firm4 et5 al
6 in
7 knowledge
8 market
9 international
10 foreign

List 6

Raw frequency list
AntConc
1 the 106,064
2 and77,542
3 of72,990
4 to47,834
5 in
42,056
6 a32,336
7 that 23,092
8 is
21,2459 for
17,30310 as
14,329

List 2

Keyword list
Sketch Engine
Brown
Midway (1000)
1 firms
2 firm
3 export
4 foreign
5 subsidiary
6 internationalization
7 FDI
8 subsidiaries
9 markets
10 MNEs

Keyword list
Sketch Engine
BAWE
Midway (1000)
1 firms
2 firm
3 export
4 table5 variables
6 international
7 markets
8 knowledge
9 foreign
10 market

List 3

List 4

Keyword list
Sketch Engine
BAWE
Rare (0.01)
1 OFDI2 offshoring
3 Vahlne
4 multinationality
5 full-size
6 MathML
7 Kogut
8 BOP
9 MathJAx
10 Ghoshal

Keyword list
Sketch Engine
BAWE
Midway (1000)
1 firms
2 firm
3 export
4 table5 variables
6 international
7 markets
8 knowledge
9 foreign
10 market

Keyword list
Sketch Engine
BAWE
Common (1 million)
1 and
2 firms
3 firm4 foreign
5 knowledge
6 international
7 market8 country
9 table10 performance

List 7

List 8

List 9

Table 3: Comparison of Brown and BAWE
Brown corpus
British Academic Written
English Corpus (BAWE)
circa 1960s
circa 2000s
General corpus
Academic corpus
American English British English
1,000,000 words 6,506,995 words

3: Statistical formula

Raw frequency list
Sketch Engine
1 the 106,022
2 and77,508
3 of72,733
4 to47,454
5 in
41,791
6 a32,007
7 that 23,092
8 is
21,2499 for
17,29310 as
14,309References
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