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Part of
Challenges in Corpus Linguistics: Rethinking corpus compilation and analysis
Edited by Mark Kaunisto and Marco Schilk
[Studies in Corpus Linguistics 118] 2024
► pp. 171–172

Subject index

A

  • accessibility 5, 30, 73, 75, 77–79, 81, 89, 91–97, 100, 109–110, 112
  • affinity propagation153
  • annotation 11–12, 16, 28–29, 40, 52, 55–58, 61–64, 75, 83, 90, 94–95, 142–143, 145, 148, 153–155, 161, 164–165
    • contact influence 148, 153–154, 161–165
    • compounds17
    • learner errors 61–62
    • multilingual language use 57–58, 64
    • named entities 35–40, 42
    • part-of-speech (POS) 11–17, 28, 75, 83

B

  • balance 11, 22–23, 29, 74, 76, 78, 84, 93
  • big-data(see very large corpora)
  • bilingualism 145, 149, 154, 156, 160–163
  • British Library Newspapers database 69–85
  • British National Corpus (BNC) 4, 21, 40–43, 45–48, 51, 98, 100, 127, 135
  • Brown Corpus 39, 72

C

  • calquing 56, 58, 61, 63
  • Clean Corpus of Historical American English51
  • codeswitching 56, 58, 158, 160, 165
  • collocation 36–37, 48–51, 83, 126, 137
  • common nouns 37–38, 40–45, 158
  • compilation 2, 4–6, 9–10, 16, 22, 29–30, 56, 59, 63–65, 68–69, 76–79, 85, 93–94, 98–101, 109–110, 131, 135, 137–138
  • computational linguistics 39, 51
  • Constituent Likelihood Automatic Word-tagging System (CLAWS) 12, 15, 40, 45
  • contact-induced semantic shifts 62, 142–165
  • copyright 92, 96–100
  • corpus exploration151
  • Corpus of Contemporary American English (COCA) 14, 21, 29, 45, 94, 127, 134–135
  • Corpus of Global Web-based English (GloWbE) 42–44
  • Corpus of Historical American English (COHA) 13–14, 18–23, 29, 94, 127, 134

D

  • databases 4–5, 10–11, 23, 29–30, 69, 72, 74–75, 85
  • deep learning27
  • diachronic corpora 9, 12, 18, 22, 29, 45, 129, 131, 147
  • Digital Humanities 24, 30, 68–73, 75–76, 81, 85, 130
  • discourse of deficit 55–56, 61
  • dispersion 2, 20–21, 29, 42, 52, 116

E

  • Eighteenth Century Collections Online (ECCO) 11, 23–27, 30
  • EF Cambridge Open Language Database (EFCAMDAT)59

F

  • false positives 57, 91, 143, 154–155, 157–162, 165
  • formality 134, 136, 145, 154, 156, 158, 160
  • foreignizing 56, 58, 61, 63

G

  • genre 4, 6, 10–11, 22–23, 29, 58, 78–79, 93, 108–110, 115, 121, 126–139
  • genre categorization/classification 18, 126–139
  • genre evolution 15–16, 22, 132
  • God’s truth fallacy 1, 3, 24, 36, 52, 89–90, 93, 99

H

  • hapax legomena 25–26
  • Helsinki Corpus 9, 22
  • historical corpora 9–13, 15–16, 22, 27–30, 69, 131
  • historical corpus linguistics 4, 9–30, 36, 69–85
  • historical lexis / historical spelling 12, 17, 26–27
  • historical text databases 10–11, 23–27, 29–30
  • homographs 143, 156, 158–162

I

  • information extraction39
  • interlanguage 56–58, 60–63
  • International Corpus of English (ICE) 59, 62
  • International Corpus of Learner English (ICLE) 59, 62, 127

K

  • keyword analysis 74, 126, 137

L

  • language contact 56, 143, 145, 148, 155–165
  • language variation 10, 142–143, 146, 165
  • learner corpus (research) 55–65, 127
  • lengthwise analysis 106, 111, 118–120
  • lengthwise scaling 118–120
  • lexical bias 60–61
  • lexical diversity 108, 114, 122–123
  • lexical innovation 62–63
  • literary corpora127
  • literary studies130
  • loan translations144
  • Lost Generation Corpus 129, 138
  • Louvain International Database of Spoken English Interlanguage (LINDSEI)57

M

  • metadata 11, 22–24, 26–27, 29–30, 64–65, 69, 75, 81, 93, 95, 98, 116–117, 146, 156
  • multilingualism 56–58, 61–64
  • multiple correspondence analysis 120–121
  • multi-word units 36–37, 39–41, 45
  • Mystery of the Vanishing Reliability 1, 89–90, 94, 99

N

  • n-grams 59, 75, 100, 126, 137
  • named entities 36–51
  • named entity recognition 36, 39, 51, 75, 127
  • natural language processing (NLP) 6, 27, 36, 39, 51, 60, 147
  • neural networks 27, 147, 152, 164
  • neural word embeddings 142–144, 147–148, 152–153
  • News on the Web Corpus (NOW) 49, 51
  • normalization 26, 107–109, 118, 121

O

  • Open American National Corpus 98, 100
  • optical character recognition (OCR) 11–12, 24–27, 29, 74, 81–85
  • open research 89–102

P

  • Parsed Corpus of Early English Correspondence (PCEEC) 15–17, 28
  • Philologist’s dilemma 1, 9–11, 28, 89, 92–93, 99
  • POS (part-of-speech) category change(see word class change)
  • precision 2, 25, 29, 35, 39, 82–83
  • proper nouns / proper names 12, 17, 36–48, 158–160

Q

  • Quebec English 142–165
  • query building 15–16, 25–27, 29, 51
  • quotations 36–37, 58, 97–98, 101

R

  • recall 25–26, 29, 82–83
  • reference corpora 93, 96, 128
  • regional variation 43, 49, 143, 145–146, 155, 164–165
  • register 69, 73, 78–81, 84–85, 112, 136
  • register analysis 79, 84, 111, 120
  • reliability 1, 11, 24, 36, 51, 90, 94, 115, 120, 146
  • remediation 69, 71
  • replicability 64, 73, 89–92, 99, 101–102
  • representativeness 2–3, 10, 22–23, 29, 36, 68, 76–81, 84–85, 90, 92–94, 109, 127
  • resampling 93, 120–121, 123

S

  • sampling 2, 9–11, 18, 20–22, 24–27, 29, 59, 65, 69, 76–78, 80, 84, 92–93, 121–122, 145
  • semantic change 144–145, 147–148, 150–151, 165 (see also contact-induced semantic shifts)
  • Spanish Learner Language Oral Corpora (SPLLOC)58
  • special corpora 85, 127
  • stylistics 130, 137
  • (near-)synonyms 41, 45–50, 156
  • syntactic parsing16

T

  • task instruction / task effects 55–56, 59–61, 64
  • text categorization(see text type)
  • text length 106–123
  • text type 46–47, 127–135
  • text sampling(see sampling)
  • transparency 91–94
  • Twitter 110, 117, 146, 149
    • Twitter corpora 101, 121, 142–165
  • type-token ratio122

V

  • vector space models (VSMs) 147–149
  • very large corpora 9–11, 18, 27–28, 30, 93, 112, 120
  • Vocabulary-Based Discourse Unit117

W

  • word-class change 12–15, 28
  • writing prompt 55–56, 59, 64
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