PHPCLANCHILDES with regular expressions

4 matches in 4 utterances

7.01 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (189 utterances, 571 words searched).

  1. is there anything you've missed ? MOT · eng_USA_Brown/sarah015.cha, line 579 · Sarah 2;05
  2. oh # you've got lots of horsies # haven't you . MOT · eng_USA_Brown/sarah015.cha, line 894 · Sarah 2;05
  3. you've got a bell like that # haven't you ? MOT · eng_USA_Brown/sarah015.cha, line 1196 · Sarah 2;05
  4. you've already investigated all that . GLO · eng_USA_Brown/sarah015.cha, line 1275 · Sarah 2;05
  1. eng_USA_Brown/sarah015.cha, line 579 · Sarah, 2;5.30
    CHII ride horsie .
    CHII xxx .
    MOT*MOT: is there anything you've missed ?
    CHIxxx .
    CHIdoggie .
  2. eng_USA_Brown/sarah015.cha, line 894 · Sarah, 2;5.30
    CHII ride horsie .
    CHIhave my horsie .
    MOT*MOT: oh # you've got lots of horsies # haven't you .
    CHIxxx horsie .
    CHIdoggie .
  3. eng_USA_Brown/sarah015.cha, line 1196 · Sarah, 2;5.30
    CHIbell [?] .
    MOT<that's a bell> [<] .
    MOT*MOT: you've got a bell like that # haven't you ?
    CHI<yeah> [?] .
    MOThmm ?
  4. eng_USA_Brown/sarah015.cha, line 1275 · Sarah, 2;5.30
    CHIyyy Santa_Claus .
    CHIyyy ?
    GLO*GLO: you've already investigated all that .
    GLOhere # Sarah .
    CHIxxx .

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_Brown7.01
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah015.chaSarah 2;5.30571447.01

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. you've4 · 100.0%