In:Mathematical Modelling in Linguistics and Text Analysis: Theory and applications
Edited by Adam Pawłowski, Sheila Embleton, Jan Mačutek and Aris Xanthos
[Current Issues in Linguistic Theory 370] 2025
► pp. 237–239
Subject index
Published online: 13 October 2025
https://doi.org/10.1075/cilt.370.si
https://doi.org/10.1075/cilt.370.si
A
- accommodation 226, 227, 229, 232, 233
- adverb 94, 95, 113, 114, 151, 154–158
- amalgamation 22, 24, 57
- animacy 27–40
- applied sciences 165, 167, 168, 170
- arts and humanities (topic) 165, 167, 168, 170
B
- Balanced Corpus of Contemporary Written Japanesesee corpus
- bibliographic corpussee corpus
- binary representation 81–89
- bivariate Gaussian model 45, 46
- bivariate lognormal distributionsee distribution
- bootstrapping 12, 13
- Borel normality 81–88
C
- Canadian parliamentary reports219
- CapekDraCorsee corpus
- case (grammatical) 27–35, 39–40
- character profiles 217–219, 224
- chi-square test 18, 66, 72–75, 78
- Cohen’s d value 76, 77
- complexity 19, 28, 89, 101, 104–106, 110–115, 118–120, 125, 128, 130, 136, 150, 177, 178, 191–202, 206, 233
- component length 71, 72, 74
- computer-mediated communication 226, 227, 233
- conceptual grid162
- conceptualization of reality 164, 165
- construction grammarsee grammar
- convolution 207–210
- Corpus of Contemporary American Englishsee corpus
- Corpus of Czech Versesee corpus
- Corpus of Historical American Englishsee corpus
- corpus
- Balanced Corpus of Contemporary Written Japanese84
- bibliographic corpus 161–163, 165
- CapekDraCor 174–179, 180, 181
- Czech Academic Corpus63
- Czech National Corpus 90, 91, 93, 120, 173, 174, 182
- Corpus of Contemporary American English 8, 9, 11
- Corpus of Czech Verse63
- COrpus of Historical American English 10, 11
- Hansard Corpus 219–221
- National Corpus of Polish Language163
- cross-linguistic influence 119, 120
- cross-linguistic similarities167
- Czech Academic Corpussee corpus
- Czech National Corpussee corpus
- Czech-PDTsee dependency treebank
D
- Decimal Classification 162, 168
- declension 27, 28
- deep learning 207, 208, 212
- DeepL166
- dependency distance 104–114, 118–125, 149–158
- dependency grammarsee grammar
- dependency syntaxsee dependency grammar
- dependency treebank
- Czech-PDT63
- FicTree 63, 64, 66, 94
- UD_Czech-CAC 62–64, 66
- UD_Czech-CLTT 63, 64, 66
- Dewey Decimal Classification 161–163, 165, 166, 168
- diachrony 6, 7, 10, 11, 14, 142
- digital humanities166
- discourse markers 104, 106, 109, 110, 114, 115
- dispersion 17, 18, 12–25, 187
- distance
- Euclidean distance 22, 23
- Levenshtein distance 27, 28, 30, 35–39
- Mahalanobis distance23
- distribution
- bivariate lognormal distribution 43, 45–47, 54, 57
- frequency distribution 70, 72–75, 78, 91, 93, 112, 173, 182, 183, 186
- Gaussian distribution 9, 45, 46
- length distribution 60, 65, 67
- Poisson distribution 46, 48
- probability distribution21
- rank-frequency distribution 60, 61, 64–67, 95–99
- Zipf-Mandelbrot distribution 6–14, 64, 65, 67, 86–88, 91, 95
- drama 90, 93, 97–99, 101, 105, 110, 173–175, 177–179, 181–183, 188
E
- entertainment (topic) 165, 167, 168, 170
- entropy 7, 12, 13
- Euclidean distancesee distance
- European integration 191, 194–197, 202
- European Union legal document219
- Evert method 8–10, 12, 13
F
- fastText 166, 212
- fiction 63, 90, 91, 93, 96, 97, 99, 101, 169, 178, 179
- fictional textsee fiction
- FicTreesee dependency treebank
- fitting functions 8, 10, 12, 13, 54, 64, 66, 86
- frequency distributionsee distribution
- frequency spectrum 7, 8, 14
G
- Gaussian copula 44, 48, 49, 54, 57
- Gaussian distributionsee distribution
- gender (grammatical) 27–40, 130, 132
- genre 17, 40, 63, 90, 91, 93, 94, 96–102, 133, 135, 159, 173, 174, 177–179, 182, 194, 207, 218, 221, 223
- geography (topic) 165, 167, 168, 170
- grammar
- dependency grammar 60, 91, 104, 105, 107, 114, 119, 149, 151, 158, 159
- construction grammar7
- phrase structure grammar91
- gravity 217, 222–224
- Gutenberg Project220
H
- Hansard Corpussee corpus
- hapax legomenon 8, 11, 64–66
- hapax token ratio 6, 11–13
- Heaps’ lawsee law
- Herdan’s lawsee law
- hierarchical structure 60, 91, 219
- history (topic) 165, 167–170
I
- ISBN163
J
- journalistic texts 63, 90, 96, 98
K
- keyness 17–20, 22–25
- keyword 18, 20, 25, 161, 162, 166, 167, 173, 177, 180, 183–185, 188
- knowledge ontology 161, 162
- Kullback-Leibler divergence 17, 21, 22
L
- language change14
- large bibliographies161
- law
- Heaps’ law7
- Herdan’s law7
- Menzerath-Altmann law 43–55, 57, 60, 61, 67
- least effort principlesee principle of least effort
- leisure magazines 90, 93, 98, 99, 101
- length distributionsee distribution
- length of the componentsee component length
- Levenshtein distancesee distance
- lexical fieldsee semantic field
- linear dependency segment 60–67
- linear regressionsee regression
- literature
- popular 93, 98
- professional 90, 93, 97, 98, 101
- scientific 93, 98
- log-likelihood ratio 10, 17, 18
M
- machine learning 149, 151, 157–159
- Mahalanobis distancesee distance
- MARC-21166
- mathematics (topic) 165, 167–170
- maximum likelihood8
- mean dependency distance 104–106, 108–114, 118–125, 149–155, 157, 158
- MeCab 73, 86
- medicine (topic) 165, 167, 168, 170
- Menzerath-Altmann lawsee law
- mora 73–75, 78
- morphology 6, 14, 27–29, 40, 73, 146, 174
- multidimensional scaling 217–219, 224
- multiword expression166
N
- National Corpus of Polish Languagesee corpus
- natural language processing 165, 166, 207
- natural sciences (topic) 165, 167–170
- neural network 207, 208
- noun declension 27, 28
- noun 27–31, 71, 94, 108, 132, 154–157, 163, 166, 167, 178, 179, 181–183, 187, 188, 193, 194, 196, 198, 206
- novel 29, 90, 93, 97, 98, 101, 177, 178, 221, 222
O
- onomastics 138, 142, 143, 146
- ontology (semantic) 161, 162, 165, 168, 169
P
- paralinguistic cues 226–228, 233
- Parallel universal dependencies63
- parliamentary debate 191, 195, 196, 202
- personal names139
- philosophy (topic) 165, 167–170
- phoneme 43–46, 48, 50–52, 54–57, 60, 132
- phrase structure grammarsee grammar
- poetry 63–66, 90, 93, 98, 101, 177, 221, 223
- Poisson distributionsee distribution
- political discourse 191–193, 195, 211–215
- political science 194, 207, 208, 211, 214
- popular literaturesee literature
- Principal Component Analysis 129, 166, 168, 191, 197–199
- principle of least effort 39, 110, 119, 193
- probabilistic translational lawsee law
- probability distributionsee distribution
- productivity 6–8, 10–14
- professionalsee literature
- psychology (topic) 165, 167, 168, 170
- PyMarc166
R
- randomness 81–84, 88, 89
- rank-frequency distributionsee distribution
- register 81, 82, 84–88
- regression 44–47, 74, 138, 142, 144–146, 197, 199, 200, 231, 232
- relative frequency 18, 19, 33–35, 37–39, 94, 95, 182
- religion (topic) 165, 167, 168, 170
- resampling 8, 11–14
- residual sum of squares49
- Romanian Online Dialect Atlas224
S
- Sapir-Whorf hypothesis 164, 170
- scientific literaturesee literature
- second language acquisition118
- self-attention 209, 210, 214
- semantic domain 161, 163–165, 167–171
- semantic field 163–166
- sentence structure 72, 91, 104, 118, 120, 178
- sentiment analysis 73, 185, 186
- short story 90, 93, 98, 101, 177, 178
- similarity (semantic, cosine) 28, 165–168, 171, 213, 223, 224, 227, 233
- social sciences (topic) 164, 165, 167, 168, 170
- specificity score 173, 186, 187
- spectrumsee frequency spectrum
- sports (topic) 165, 167, 168, 170
- spread 23, 96, 217, 219–224
- starburst 218, 221
- stochastic process 43–45, 58, 54, 57
- stylometry 57, 91, 93, 128, 129, 136, 221
- synergetic linguistics56
- syntactic complexity 104–106, 110–113, 118–120, 125, 150, 194
- syntactic function 66, 90, 94, 96–99, 102, 104, 110, 113, 114
- syntax 91, 112, 113, 122, 131, 135, 178
T
- technology (topic) 165, 167, 168, 170
- TED talks 104, 106
- text genresee genre
- TF-IDF184
- theology (topic) 165, 167, 168, 170
- token 6–14, 20, 21, 30, 64–66, 94, 174, 177–179, 209, 210, 212, 228
- translation regularity 149–151, 158
- t-test 11, 19, 74, 76–78, 122, 134, 154, 155, 197, 201
- TTRsee type-token ratio
- type character ratio 86, 88
- type-token ratio 86, 88, 150, 177, 178, 193, 196
U
- UD_Czech-CACsee dependency treebank
- UD_Czech-CLTTsee dependency treebank
- UDPipe 28, 122, 166
- Universal Decimal Classification 161–163, 165, 166, 168
- Universal dependencies 62, 63. 107, 122
V
- valency 70, 72, 73, 78
- verb 92, 109, 110, 114, 153, 154, 156, 157, 162, 163, 174, 177–179, 182–184, 187, 188, 192–194, 196, 198, 206
W
- Welch’s t-tests see t-test
- WhatsApp 226–228, 232
- word embedding166
- word order 61, 62, 72, 78, 107, 155, 181, 209
- word2vec 166–168, 208
- WordSmith18
Z
- Zipf’s lawsee distribution
- Zipfian distributionsee distribution
- Zipf-Mandelbrot distributionsee distribution
- Zipf-Mandelbrot lawsee Zipf’s distribution
