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Part of
Textbook English: A multi-dimensional approach
Elen Le Foll
[Studies in Corpus Linguistics 116] 2024
► pp. 293–293

Index

A
  • acceptability judgments58
  • analysis of variance, ANOVA 134, 152
  • annotation 69, 75–84, 112, 118–120, 243–248
  • authentic texts/language 7–11, 36, 88, 209, 212–225, 231–233
  • authenticity 7–11, 29–30, 40
B
  • Biber tagger 115–116, 238
  • British English 87–91
  • British National Corpus 2014, BNC2014 51, 55, 86–87, 90–92, 119–121, 135, 209–210, 243
  • British National Corpus 1994, BNC1994 29–30, 32–34, 45, 50–51, 55, 57, 63–64, 91, 92
C
  • cognitive linguistics 11–12, 45–46
  • collocation 12, 35, 45–46, 88–89, 211–212, 218, 220–222
  • Common European Framework of Reference for Languages, CEFR 6, 19, 26
  • construction grammar 11–14, 212
  • Corpus of Contemporary American English, COCA 32, 88, 211–213, 219–222
  • corpus-driven2
  • cross-validation 139, 238–239
  • curriculum 1, 7–8, 16–18, 232
D
  • data-driven learning 22, 230, 236
  • delexical constructions 48, 51
  • disfluencies 184, 188, 200
E
  • English as a foreign language, EFL5
  • English as a Lingua Franca, ELF 6, 87–90
  • English as a Second Language, ESL5
  • English for Academic Purposes, EAP 23, 29–30, 51–52, 58, 80
  • exploratory factor analysis, EFA 100, 112, 124–126, 129, 141f., 148, 240
F
  • factor analysissee EFA and PCA
  • features 114–124, appendix C
  • finite verb phrase 123, 168–169
  • France 6–7, 17–19, 71–75
G
  • genre 78–79
  • Germany 6–7, 15–19, 71–75
I
  • input 2, 5, 9–21, 41–44, 65, 70, 86, 90, 226–227, 249
  • International Standard Classification of Education, ISCED 6, 71
L
  • learner corpus 24, 41–42, 45, 216, 235, 248–250
  • lexical approach18
  • lexical bundle 51–53, 212
  • linguistic features 114–124, appendix C
  • listening 7, 37, 39, 61, 214
M
  • materials design 23, 220, 231, 233–236, 250
  • mixed-effects modeling 134–135, 151–152, 171, 241, 247
  • Multidimensional Analysis Tagger, MAT 108, 138, 238
  • Movies corpus211
  • Multi-Feature Tagger of English, MFTE 114–123, 138–139, 242, 244
  • multiple correspondence analysis112
N
  • n-gram115
  • native speaker 5–6, 9, 14, 36, 40, 43, 62, 64–65, 87–89, 250
O
  • Open Science 35, 68–69, 138, 241–242
  • optical character recognition, OCR 73–75, 80, 94, 118
P
  • Perl 116, 119, 138
  • pedagogic corpus 55, 70
  • phrasal verbs 32, 45, 50, 187, 190, 198
  • phrasemes 51, 55, 249
  • principal component analysis, PCA 125–126, 133, 139, 240–242, 247
  • proficiency level 6, 50, 66–67, 77, 86, 109, 134, 146–149, 152, 156, 161–165, 175–176, 180–182, 192
  • Python 77, 94, 138
R
  • R 69, 126, 135, 241
  • reference corpus/corpora 32–33, 38–39, 50, 60–63, 84–95, 243
  • regression 134–136, 175–176, 247
  • reliability 28–29, 34, 68, 81, 89, 119–123, 126, 244
  • replicability 68–69, 108, 114, 124, 132, 138–139, 237–242
  • representativeness 71–75, 86, 90–93, 97, 113, 127, 242, 249–251
  • reproducibility 68–69, 138–139, 241–242, 248, appendices B–H
S
  • Spain 17, 45, 71–74
  • support verb constructions 48, 51
  • syllabus 16–18, 23, 36, 48, 218, 249
T
  • tagger 114–123, 138–139, 242, 244
  • tasks 7–9, 15, 19, 25–28, 40, 46, 82, 110–111, 156, 226, 250
  • teachers and teacher education 15–20, 59, 60, 70–75, 88–90, 196–197, 205, 209–212, 219–222, 227–233
  • text type 63, 78–79, 205
  • text units 112–114
  • textbook market 19, 56, 72–73, 214
  • TV language 210–214
U
  • usage-based linguistics 11–14, 62, 65, 212, 219, 250