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Intensification for discursive evaluation: a corpus-pragmatic view

  • Yun Pan

    Yun Pan (PhD, Newcastle University, UK) is currently a Lecturer in Applied Linguistics at Shanghai Maritime University, China. Her research interests lie at the intersection of Corpus Linguistics, pragmatics, and sociolinguistics. She has published in international peer-reviewed journals, such as Applied Linguistics Review, Pragmatics & Cognition, Pragmatics, and Translation & Interpreting.

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Published/Copyright: December 6, 2021
Text & Talk
From the journal Text & Talk Volume 42 Issue 3

Abstract

The phenomenon of intensification is pervasive in natural language use. Previous research has extensively discussed what intensifiers are and how they are associated with semantic developments. Corpora prove to be a useful tool to examine the semantic dimension of intensifiers. What has been overlooked, however, is the internal structure(s) of meaning conveyed by “intensifier + adjective” constructions in naturally occurring text and speech. The semantic relationship between the intensifier and the modified adjective needs to be made more explicit to address the pragmatics of intensification. Using BNC Sampler (a part-of-speech tagged corpus of general English) this study examines the most frequently used adjective intensifiers in both written and spoken discourse. Concordance lines generated for the adjective intensifiers are used to illustrate evaluative expressions in context. The observation contributes to debates on the pragmatics of intensifiers for evaluative meaning construction and transmission.

Keywords: Corpus Linguistics; evaluation; intensification; intensifier; pragmatics
Corresponding author: Yun Pan, Foreign Languages School, Shanghai Maritime University, Shanghai, China, E-mail:

About the author

Yun Pan

Yun Pan (PhD, Newcastle University, UK) is currently a Lecturer in Applied Linguistics at Shanghai Maritime University, China. Her research interests lie at the intersection of Corpus Linguistics, pragmatics, and sociolinguistics. She has published in international peer-reviewed journals, such as Applied Linguistics Review, Pragmatics & Cognition, Pragmatics, and Translation & Interpreting.

Appendix

Adjective collocates of very, so, quite, and too in BNC Sampler.

No. Word Total no. in sub corpus Expected collocate fŕeq. Observed collocate fie q. In no. of texts Log Ratio (filtered)
1 frightened 25 0.143 6 3 5.774
2 tired 49 0.281 11 8 5.649
3 helpful 24 0.138 5 4 5.511
4 exciting 42 0.241 7 5 5.115
5 nice 77 0.442 12 6 5
6 pleased 52 0.298 8 6 4.978
7 fast 72 0.413 11 8 4.966
8 angry 55 0.316 8 4 4.883
9 careful 50 0.287 7 5 4.818
10 sad 58 0.333 8 5 4.793
11 unhappy 40 0.23 5 5 4.63
12 difficult 214 1.228 26 14 4.583
13 dangerous 73 0.419 8 3 4.415
14 quiet 113 0.648 11 6 4.224
15 useful 110 0.631 10 7 4.115
16 interesting 107 0.614 9 9 3.993
17 attractive 92 0.528 7 3 3.835
18 happy 144 0.826 10 7 3.693
19 limited 88 0.505 6 4 3.665
20 important 338 1.939 22 16 3.593
21 kind 186 1.067 12 7 3.579
22 easy 148 0.849 9 8 3.488
23 strange 118 0.677 7 6 3.45
24 bad 120 0.688 7 6 3.425
25 strong 213 1.222 12 9 3.371
26 low 144 0.826 8 8 3.35
27 simple 127 0.729 7 5 3.338
28 small 599 3.436 32 18 3.29
29 hard 175 1.004 9 6 3.232
30 good 748 4.291 38 18 3.214
31 similar 193 1.107 8 6 2.906
32 large 419 2.404 17 11 2.874
33 little 630 3.614 24 19 2.779
34 few 458 2.627 16 11 2.649
35 different 440 2.524 15 10 2.613

  1. Adjective collocates of very in BNC Sampler written sub corpus.

No. Word Total no, in sub coipus Expected collocate freq. Observed collocate fieq. In no. of texts Log Ratio (filtered)
1 sensitive 10 0.071 5 4 7.128
2 attractive 17 0.121 6 5 6.253
3 grateful 15 0.107 5 5 6.128
4 disappointed 19 0.135 6 4 6.012
5 keen 35 0.249 11 7 6.002
6 difficult 205 1.455 57 28 5.751
7 pleased 48 0.341 13 10 5.699
8 brief 25 0.178 6 4 5.465
9 busv 51 0.362 12 7 5.427
10 slight 22 0.156 5 2 5.362
11 interesting 100 0.71 22 17 5.302
12 elevar 50 0.355 11 7 5.302
13 careful 75 0.532 16 11 5.245
14 important 230 1.633 49 28 5.243
15 useful 38 0.27 8 7 5.221
16 helpful 24 0.17 5 5 5.202
17 proud 25 0.178 5 4 5.128
18 similar 55 0.39 11 11 5.128
19 streng 61 0.433 12 8 5.098
20 strange 36 0.256 7 6 5.077
21 limited 26 0.185 5 3 5.057
22 positive 30 0.213 5 4 4.806
23 sensible 31 0.22 5 4 4.749
24 m 31 0.22 5 4 4.749
25 quiet 56 0.398 9 6 4.743
26 expensive 66 0.469 10 8 4.642
27 nice 780 5.537 111 34 4.536
28 good 1,799 12.771 244 63 4.456
29 upset 37 0.263 5 5 4.45
30 low 60 0.426 8 7 4.427
31 happy 135 0.958 18 17 4.427
32 lucky 57 0.405 7 5 4.291
33 simple 58 0.412 7 6 4.263
34 dark 44 0.312 5 2 4.164
35 close 128 0.909 14 9 4.102
36 warm 90 0.639 9 6 3.958
37 high 171 1.214 17 10 3.949
38 serious 55 0.39 5 4 3.806
39 easy 125 0.887 11 10 3.754
40 sheet 106 0.738 9 8 3.728
41 large 126 0.895 10 9 3.592
42 small 194 1.377 15 14 3.551
43 common 87 0.618 6 3 3.373
44 quick 118 0.838 7 5 3.141
45 special 140 0.994 8 7 3.083
46 hard 224 1.59 12 12 2.985
47 big 572 4.061 19 14 2.265

  1. Adjective collocates of very in BNC Sampler spoken sub corpus.

No. Word Total no. in sub corpus Expected collocate freq. Observed collocate freq. In no. of texts Log Ratio (filtered)
1 pleased 52 0.084 5 4 6.036
2 sorry 75 0.121 5 5 5.462
3 bright 95 0.154 5 4 5.099
4 hard 175 0.283 8 7 4.885
5 bad 120 0.194 5 5 4.745
6 kind 186 0.301 5 4 4.091
7 important 338 0.547 9 8 4.077
8 young 322 0.521 8 5 3.974
9 good 748 1.211 12 8 3.33
10 great 464 0.751 7 6 3.24

  1. Adjective collocates of so in BNC Sampler written sub corpus.

No. Word Total no. in sub corpus Expected collocate freq. Observed collocate freq. In no. of texts Log Ratio (filtered)
1 stupid 75 0.138 11 8 6.546
2 funny 153 0.281 17 10 6.086
3 pleased 48 0.088 5 4 5.982
4 busy 51 0.094 5 4 5.884
5 tired 69 0.127 6 4 5.694
6 bad 271 0.498 15 11 4.993
7 hot 102 0.187 5 4 4.808
8 quick 118 0.217 5 5 4.588
9 high 171 0.314 5 5 4.033
10 fine 179 0.329 5 1 3.965
11 hard 224 0.411 5 5 3.633
12 nice 780 1.433 12 9 3.086
13 long 562 1.032 7 7 2.777
14 good 1,799 3.304 22 17 2.75

  1. Adjective collocates of so in BNC Sampler spoken sub corpus.

No. Word Total no. in sub corpus Expected collocate freq. Observed collocate freq. In no. of texts Log Ratio (filtered)
1 sure 165 0.112 6 4 5.793
2 clear 246 0.167 6 6 5.199
3 different 440 0.299 9 7 4.939

  1. Adjective collocates of quite in BSC Sampler written sub corpus.

No. Word Total no. in sub corpus Expected collocate freq. Observed collocate freq. In no. of texts Log Ratio (filtered)
1 happy 135 0.275 27 18 6.937
2 pleased 48 0.098 5 5 5.832
3 honest 68 0.139 7 5 5.813
4 interesting 100 0.204 10 9 5.767
5 Kkelv 61 0.124 6 4 5.74
6 funny 153 0.312 12 7 5.382
7 sure 488 0.994 31 19 5.055
8 easy 125 0.255 7 7 4.861
9 short 104 0.212 5 4 4.629
10 clear 110 0.224 5 5 4.544
11 hard 224 0.456 10 7 4.517
12 difficult 205 0.418 8 5 4.315
13 nice 780 1.589 17 13 3.449
14 good 1,799 3.664 38 23 3.402
15 enough 372 0.758 7 5 3.232
16 big 572 1.165 10 9 3.124
17 different 453 0.923 7 6 2.943

  1. Adjective collocates of quite in BNC Sampler spoken sub corpus.

No. Word Total no. in sub corpus Expected collocate freq. Observed collocate freq. In no. of texts Log Ratio (filtered)
1 busy 31 0.038 6 5 7.629
2 slow 61 0.074 5 5 6.202
3 late 267 0.323 21 14 6.137
4 easy 148 0.179 5 4 4.85
5 far 405 0.491 10 8 4.384
6 close 284 0.344 6 5 4.154
7 old 555 0.672 11 8 4.06
8 small 599 0.726 10 8 3.807

  1. Adjective collocates of too in BNC Sampler written sub corpus.

No. Word Total no. in sub corpus Expected collocate freq. Observed collocate freq. In no. of texts Log Ratio (filtered)
1 late 137 0.224 24 17 7.017
2 bad 271 0.444 44 25 6.886
3 hot 102 0.167 12 7 6.346
4 heavy 68 0.111 7 4 6.129
5 busy 51 0.084 5 4 6.051
6 high 171 0.28 16 8 5.977
7 cold 142 0.232 10 8 5.53
8 early 116 0.19 6 4 5.056
9 close 128 0.21 6 5 4.907
10 small 194 0.318 7 6 4.513
11 big 572 0.936 19 15 4.389
12 young 168 0.275 5 2 4.226
13 hard 224 0.367 6 5 4.069

  1. Adjective collocates of too in BNC Sampler spoken sub corpus.

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Received: 2020-04-05
Accepted: 2021-11-23
Published Online: 2021-12-06
Published in Print: 2022-05-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/text-2020-0046
Keywords for this article
Corpus Linguistics; evaluation; intensification; intensifier; pragmatics

Articles in the same Issue

  1. Frontmatter
  2. Full Length Articles
  3. “Un projet de justice et de progrès social”: President Macron’s 2020 new year message to the nation. A linguistic viewpoint
  4. Proximal versus distant suffering in TV news discourses on COVID-19 pandemic
  5. Reframing Arabic narratives on Daesh [ISIS] textually through translation: MEMRI’s translation as a case study
  6. Non-directive play therapy with autistic adolescents: a qualitative study of therapists’ interactional practices
  7. Intensification for discursive evaluation: a corpus-pragmatic view
  8. Entextualizing high energy texts: an exploration of modal shift on a Chinese online video-sharing website Bilibili
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