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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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# Collocate POS A TTEST MI3 CHISQ JD R AWT fAWT Details Equivalents
1. be 's
are
be
been
being
is
was
were 		Verb 374 -70.46 14.88 1,602.29 0.88 84 647,504 83.95
2. have had
has
have
having 		Verb 77 -52.55 9.73 440.66 0.83 44 1,916,043 28.37
3. do did
do
does
doing 		Verb 18 -48.93 4.69 199.73 0.73 14 4,879,212 11.14
4. would would Verb 14 -29.29 4.47 99.57 0.73 13 8,770,278 6.2
5. can ca
can 		Verb 12 -28.51 3.96 90.02 0.7 11 9,107,420 5.97
6. make made
make
makes 		Verb 9 -26.54 3.04 72.79 0.68 9 12,143,081 4.48
7. say said
say
saying
says 		Verb 25 -21.78 6.87 90.96 0.74 19 5,771,447 9.42
8. get get
getting
got 		Verb 13 -21.37 4.61 67.12 0.61 9 11,512,192 4.72
9. go go
goes
going
went 		Verb 18 -18.3 5.93 64.21 0.72 14 7,273,039 7.47
10. could could Verb 14 -14.35 5.34 43.17 0.71 12 7,038,726 7.72
11. take take
taken
taking
took 		Verb 17 -13.67 6.07 43.96 0.68 13 7,697,741 7.06
12. see saw
see
seen
sees 		Verb 21 -12.49 6.89 42.52 0.73 17 5,495,548 9.89
13. find find
finding
found 		Verb 10 -9.64 4.63 23.16 0.7 10 11,225,795 4.84
14. may may Verb 14 -9 5.85 24.01 0.71 12 7,413,219 7.33
15. will will Verb 51 -8.22 10.24 32.12 0.71 22 3,741,865 14.53
16. come came
come
comes
coming 		Verb 23 -7.96 7.64 24.1 0.74 17 8,157,283 6.66
17. might might Verb 6 -7.75 3.11 14.51 0.6 6 22,509,649 2.41
18. mean mean
means
meant 		Verb 7 -7.7 3.65 15.24 0.63 7 12,625,025 4.31
19. become became
become
becomes 		Verb 9 -6.31 4.71 12.92 0.55 6 23,482,359 2.31
20. should should Verb 19 -6.23 7.22 16.11 0.73 15 6,717,681 8.09
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