PHPCLANCHILDES with regular expressions

8 matches in 6 utterances

4.97 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (462 utterances, 1,610 words searched).

  1. where [/] where'd you go yesterday ? MOT · eng_USA_Brown/sarah074.cha, line 744 · Sarah 3;08
  2. where [/] where'd you go yesterday ? MOT · eng_USA_Brown/sarah074.cha, line 744 · Sarah 3;08
  3. where ? MOT · eng_USA_Brown/sarah074.cha, line 752 · Sarah 3;08
  4. where was that in town ? MOT · eng_USA_Brown/sarah074.cha, line 764 · Sarah 3;08
  5. down in the country where my # um # where um # um # John's friend lives . ANN · eng_USA_Brown/sarah074.cha, line 803 · Sarah 3;08
  6. down in the country where my # um # where um # um # John's friend lives . ANN · eng_USA_Brown/sarah074.cha, line 803 · Sarah 3;08
  7. where ? MOT · eng_USA_Brown/sarah074.cha, line 808 · Sarah 3;08
  8. where my xxx ? ANN · eng_USA_Brown/sarah074.cha, line 1492 · Sarah 3;08
  1. MOTdid you tell Ann_Marie that you saw rabbits yesterday ?
    ANNxxx night .
    MOT*MOT: where [/] where'd you go yesterday ?
    ANNum # no Saturday .
    MOTdown Margie's ?
  2. MOTdown Margie's ?
    ANNno .
    MOT*MOT: where ?
    ANNwe [/] # we went Sunday down Margie's .
    ANNxxx .
  3. ANNyeah # I was the rabbit .
    ANNxxx .
    MOT*MOT: where was that in town ?
    MOTin Boston ?
    MOTdown the basement .
  4. MOTthese were real # real rabbits .
    ANNwant to know somethin(g) ?
    ANN*ANN: down in the country where my # um # where um # um # John's friend lives .
    ANNshe um +...
    MOTwhere ?
  5. ANNdown in the country where my # um # where um # um # John's friend lives .
    ANNshe um +...
    MOT*MOT: where ?
    ANNJohn's friend .
    ANNshe has a dog # (a)n(d) a +...
  6. MOTyou all through ?
    CHIbug .
    ANN*ANN: where my xxx ?
    CHIno: .
    MOToh .

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_Brown4.97
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah074.chaSarah 3;8.201,610684.97

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. where8 · 100.0%