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HASK | COLLOCATION  DATABASES
Citation Please cite this paper in publications using HASK for the BNC: Pezik, Piotr. Graph-Based Analysis of Collocational Profiles. In Phraseologie Im Wörterbuch Und Korpus (Phraseology in Dictionaries and Corpora), edited by Vida Jesenšek and Peter Grzybek, 227–43. ZORA 97. Maribor, Bielsko‑Biała, Budapest, Kansas, Praha: Filozofska fakuteta, 2014.
150,045entries containing2,875,407combinations...
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MI3
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# Collocate POS A TTEST MI3 CHISQ JD R AWT fAWT Details Equivalents
1. be 're
's
are
be
been
being
is
was
were
Verb 306 -31.23 15.04 524.53 0.83 74 1,025,373 53.01
2. do did
do
does
Verb 14 -25.84 4.63 88.3 0.65 9 12,944,282 4.2
3. have 'd
've
had
has
have
having
Verb 71 -22.91 10.4 157.38 0.83 40 2,173,916 25.01
4. would 'd
would
Verb 8 -18.61 3.08 46.82 0.66 8 11,552,888 4.71
5. say said
say
says
Verb 11 -16.5 4.34 46.77 0.71 11 9,580,543 5.67
6. make made
makes
making
Verb 6 -15.3 2.31 32.88 0.6 6 16,457,094 3.3
7. take take
taken
takes
Verb 5 -13.87 1.8 27.09 0.56 5 35,414,529 1.53
8. will will Verb 12 -12.08 5 33.22 0.68 10 11,876,338 4.58
9. see saw
see
seeing
sees
Verb 8 -10.74 3.74 24.42 0.54 6 12,875,473 4.22
10. come came
come
comes
coming
Verb 6 -9.8 2.85 19.44 0.6 6 19,194,937 2.83
11. should should Verb 5 -7.89 2.46 13.87 0.56 5 21,900,937 2.48
12. could could Verb 15 -4.7 6.67 10.13 0.71 12 18,145,322 3
13. must must Verb 6 -3.47 3.9 5.02 0.6 6 16,890,616 3.22
14. show show
showing
shows
Verb 5 -3.2 3.36 4.24 0.56 5 33,652,107 1.62
15. may may Verb 13 -2.88 6.55 4.66 0.59 8 19,268,505 2.82
16. can can Verb 38 -2.65 9.98 5.03 0.81 27 4,518,747 12.03
17. might might Verb 6 -2.56 4.14 3.22 0.6 6 20,017,597 2.72
18. mean means
meant
Verb 7 -2.43 4.68 3.09 0.63 7 21,916,600 2.48
19. seem seem
seems
Verb 7 -2.04 4.79 2.36 0.63 7 11,672,350 4.66
20. become became
becomes
becoming
Verb 8 -2.02 5.22 2.39 0.51 5 17,306,670 3.14
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