PHPCLANCHILDES with regular expressions

7 matches in 7 utterances

3.68 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (546 utterances, 1,903 words searched).

  1. see if you can find some more of the donkey . MOT · eng_british_Manchester_I/becky27a.cha, line 353 · Becky 2;09
  2. we need a bit more of the donkey , don't we ? MOT · eng_british_Manchester_I/becky27a.cha, line 380 · Becky 2;09
  3. see if you can find the donkey's face . MOT · eng_british_Manchester_I/becky27a.cha, line 404 · Becky 2;09
  4. is [//] does this belong to the donkey ? MOT · eng_british_Manchester_I/becky27a.cha, line 407 · Becky 2;09
  5. so let's see if we can the [/] the donkey's other eye . MOT · eng_british_Manchester_I/becky27a.cha, line 533 · Becky 2;09
  6. we need the donkey . MOT · eng_british_Manchester_I/becky27a.cha, line 578 · Becky 2;09
  7. find the rest of the donkey . MOT · eng_british_Manchester_I/becky27a.cha, line 580 · Becky 2;09
  1. MOTand what about that one ?
    MOTthat one goes on there as well .
    MOT*MOT: see if you can find some more of the donkey .
    CHIhere's a donkey .
    CHIand where's um the +...
  2. CHIum +/.
    MOTa bit more +//.
    MOT*MOT: we need a bit more of the donkey , don't we ?
    MOTcan you see if you can find it ?
    CHIum I can't .
  3. MOTit goes like this .
    MOTthere .
    MOT*MOT: see if you can find the donkey's face .
    MOTis [//] does this belong to the donkey ?
    CHIno .
  4. MOTthere .
    MOTsee if you can find the donkey's face .
    MOT*MOT: is [//] does this belong to the donkey ?
    CHIno .
    CHIthere .
  5. MOTso that's alright , isn't it ?
    CHIyeah .
    MOT*MOT: so let's see if we can the [/] the donkey's other eye .
    MOTsee if you can find it on those .
    CHIthose .
  6. MOTit does go there .
    MOTit must go there .
    MOT*MOT: we need the donkey .
    MOTfind the rest of the donkey .
    CHIcan't find +/.
  7. MOTit must go there .
    MOTwe need the donkey .
    MOT*MOT: find the rest of the donkey .
    CHIcan't find +/.
    MOTis there a piece missing ?

Matches by age

One dot per transcript: matches per 1,000 words of everyone but CHI. Dots of the same child are joined.

Matches by child

Matches per 1,000 words, all transcripts of each child together, highest first.

  1. Becky eng_british_Manchester_I3.68
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_british_Manchester_I/becky27a.chaBecky 2;9.061,903773.68

What the pattern matched

1 different forms (lowercased). Useful to see what a pattern really catches: unexpected forms here usually mean the pattern is too broad.

  1. donkey7 · 100.0%