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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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Part of speech:
Collocate's part of speech:
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# Collocate POS A TTEST MI3 CHISQ JD R AWT fAWT Details Equivalents
1. have had
has
have
having 		Verb 51 -24.43 9.2 150.49 0.68 24 3,809,884 14.27
2. be 're
's
are
be
been
being
is
was
were 		Verb 334 -21.54 15.64 318.98 0.72 59 1,393,608 39.01
3. see see Verb 7 -9.74 3.39 20.58 0.26 3 38,313,076 1.42
4. make made
make
making 		Verb 10 -8.58 4.75 20.16 0.37 5 13,121,069 4.14
5. would would Verb 29 -4.23 8.88 10.26 0.66 15 7,455,696 7.29
6. could could Verb 16 -3.09 7.17 5.46 0.68 11 8,603,395 6.32
7. can can Verb 31 -2.77 9.33 5.23 0.63 12 7,927,739 6.86
8. give gave
give
given
giving 		Verb 13 -2.54 6.63 3.83 0.51 6 12,439,756 4.37
9. will will Verb 36 -1.66 9.99 2.21 0.68 16 5,966,527 9.11
10. must must Verb 9 -1.13 5.88 0.93 0.5 6 13,685,823 3.97
11. might might Verb 8 -0.87 5.61 0.59 0.57 6 16,762,430 3.24
12. cause caused
causes 		Verb 5 0.63 5.12 0.56 0.56 5 13,327,503 4.08
13. accept accept
accepted
accepts 		Verb 5 0.67 5.16 0.64 0.48 4 20,283,465 2.68
14. base based Verb 7 1.36 6.66 3.83 0.45 4 15,286,097 3.56
15. include include
included
includes 		Verb 11 1.45 7.75 3.72 0.61 8 11,512,906 4.72
16. should should Verb 28 1.64 10.15 3.92 0.52 10 9,567,198 5.68
17. may may Verb 29 1.68 10.25 4.12 0.69 18 6,526,224 8.33
18. grant grant
granted 		Verb 5 1.7 6.7 12.03 0.56 5 18,420,364 2.95
19. obtain obtain
obtained
obtaining
obtains 		Verb 7 1.8 7.25 10.07 0.63 7 10,896,408 4.99
20. incur incurred
incurring 		Verb 5 2.1 8.67 71.6 0.48 4 29,692,269 1.83
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