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Part of
Recent Advances in Multiword Units in Machine Translation and Translation Technology
Edited by Johanna Monti, Gloria Corpas Pastor, Ruslan Mitkov and Carlos Manuel Hidalgo-Ternero
[Current Issues in Linguistic Theory 366] 2024
► pp. 263–264

Index

A
  • adjectival MWEs,226, 231, 236
B
  • bracketing,79–80, 85–90, 96, 98–99, 173, 175, 184, 186–91, 193, 195
C
  • cognitive linguistics (CL),140, 197, 199, 201, 216
  • collocations,5–6, 16, 60, 62, 66, 71, 73, 110–11, 123, 126–40, 166, 199, 211, 218, 229–30
  • computational linguistics,15–16, 36–39, 55, 75, 100, 172, 195, 208, 213–15, 241–42, 259–61
  • computational treatment of multiword units,41, 156, 158
  • conceptual structure,171, 200, 202
  • construction grammar,4, 15, 76, 218–19
  • constructions,13–15, 19, 38–39, 141–42, 155–56, 158, 164–65, 171–72, 175–76, 190, 260
  • convolutional neural network (CNN),24, 38
  • corpus-based phraseology,15, 100, 104, 140, 171, 215
  • corpus-based translation studies (CBTS),3, 15, 58–59, 76
D
  • DeepL,2–3, 7–14, 18–20, 24, 26, 29, 31–35, 37–38, 40, 42, 45–46, 48, 50–53, 58, 63
  • deep learning (DL),2, 6, 11–12, 164, 216–18
  • discontinuous MWEs,19, 23, 31, 35, 239–40
E
  • electronic lexicography,76, 100, 195
  • European Association for Machine Translation (EAMT),54–55, 75, 214
F
  • formulaic language,4–5, 16, 123, 140, 218
  • frame semantics,126–27, 140, 195
G
  • Google Translate (GT),2, 7–9, 11–14, 37–38, 40, 42, 45–46, 48, 50–51, 53, 164, 249–52, 257
I
  • idiomatic expressions,10, 22, 37, 149, 199, 249, 251
L
  • lexical bundles,78, 122, 125, 218–19
  • logDice,127, 129, 131–35, 137, 166
M
  • machine learning,6, 213
  • Microsoft Bing Translator,40, 42, 45–46, 48, 50–51, 53
  • middle construction,156–72
  • machine translation (MT),2–3, 15–16, 18, 36, 39–44, 50–59, 66–70, 73–75, 77, 156–59, 162, 164–65, 214, 242–43, 258–60
  • multiword expression (MWE),4–6, 15, 18–20, 22–27, 29–31, 35–39, 55, 105–6, 218–20, 223–46, 254–56, 258–60
  • multiword term (MWT),79–85, 87–88, 91, 95–96, 98–100, 174–75, 184, 186–94
  • multiword unit (MWU),3–17, 36, 40–48, 50–55, 58, 73, 75, 156, 158–59, 162, 164–66, 169–70, 258, 260
N
  • natural language processing (NLP),15, 18, 37, 39–41, 54–56, 79–80, 195, 198, 214–15, 237, 239–42, 244, 258–59
  • neural networks,6–7, 14, 18, 37, 260
  • neural machine translation (NMT),2–19, 33–40, 43, 54–61, 63–77, 164, 169–70, 218, 238, 240, 242–46, 258–61
  • nominal MWEs,225, 227
P
  • pattern,23–25, 94, 125, 135, 141–43, 146–53, 156–58, 165, 168–70, 181, 254–55
  • phrasal verbs,245, 249, 260
  • phrase-based machine translation (PBMT),36, 218, 243
  • phrase-based statistical machine translation (PBSMT),39, 218
  • phrasemes,107–11, 113, 115–16, 118–19, 121–22, 249
  • phraseological units,2, 5–6, 60, 73, 125, 140, 199, 202, 211, 213, 216
  • phraseology,2–9, 11, 14–15, 57, 60, 70–71, 73–76, 103–6, 123, 174, 194, 218–19
  • phrases,9–10, 12–13, 116, 159, 165, 167, 175, 206
  • post-editing,3, 15, 18, 57–77, 121
  • proverbs,104, 110, 218, 223, 228–29, 236–37
S
  • semantic frames,124, 126, 128–29, 135, 137–39, 175–76, 183, 194
  • Sketch Engine,11–13, 24, 80, 128, 140, 143, 159, 166–67, 170, 174, 195
  • statistical machine translation (SMT),2, 15, 38, 43, 54–55
  • speech formulae,111, 113, 116–17, 122
T
  • Terminological Knowledge Base (TKB),99–100, 173–74, 193–95
  • ternary compounds,184, 187–89, 191, 193
  • translation technology (TT),28, 32, 36, 41, 55, 75, 77, 105, 121–22, 216, 258, 260
V
  • verbal MWEs,5, 38, 225–27, 233, 235, 245–61
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