PHPCLANCHILDES with regular expressions

6 matches in 6 utterances

3.69 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (375 utterances, 1,625 words searched).

  1. I don't know how you're gonna be able to get them out with the rest of the page off . MOT · eng_USA_Brown/sarah092.cha, line 274 · Sarah 4;01
  2. see # you <cut this> [//] # cut the rest of them out . MOT · eng_USA_Brown/sarah092.cha, line 632 · Sarah 4;01
  3. out the rest of them out . MOT · eng_USA_Brown/sarah092.cha, line 636 · Sarah 4;01
  4. punch the rest of them out . MOT · eng_USA_Brown/sarah092.cha, line 712 · Sarah 4;01
  5. yeah # well # you know how to do them too . MOT · eng_USA_Brown/sarah092.cha, line 860 · Sarah 4;01
  6. alright # you start punchin(g) them out . MOT · eng_USA_Brown/sarah092.cha, line 1043 · Sarah 4;01
  1. MOTit's no good .
    CHIwhy ?
    MOT*MOT: I don't know how you're gonna be able to get them out with the rest of the page off .
    CHIlike yesterday ?
    MOTmmhm .
  2. CHIwhere does this go ?
    MOTyeah # that goes on the next page .
    MOT*MOT: see # you <cut this> [//] # cut the rest of them out .
    MOTalright # you put this here .
    MOTout the rest of them out .
  3. MOTsee # you <cut this> [//] # cut the rest of them out .
    MOTalright # you put this here .
    MOT*MOT: out the rest of them out .
    CHII'm doin(g) it easy # huh ?
    MELmmhm .
  4. CHIyou did this .
    MOTyeah # well # come on .
    MOT*MOT: punch the rest of them out .
    MOTyou'll be all day .
    CHIMommy .
  5. CHIshe knows how to do em .
    MOThere # Sarah .
    MOT*MOT: yeah # well # you know how to do them too .
    MOTyou stick this one in first xxx .
    MELhow old is he ?
  6. MOThere .
    CHIthis xxx .
    MOT*MOT: alright # you start punchin(g) them out .
    ANNmmhm .
    CHII put the light on .

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. Sarah eng_USA_Brown3.69
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah092.chaSarah 4;1.181,625663.69

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. them6 · 100.0%