PHPCLANCHILDES with regular expressions

6 matches in 6 utterances

3.02 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (463 utterances, 1,986 words searched).

  1. look at your hair . MOT · eng_USA_Brown/sarah058.cha, line 537 · Sarah 3;04
  2. look it . KEN · eng_USA_Brown/sarah058.cha, line 549 · Sarah 3;04
  3. look . KEN · eng_USA_Brown/sarah058.cha, line 559 · Sarah 3;04
  4. look like a rag mop . KEN · eng_USA_Brown/sarah058.cha, line 572 · Sarah 3;04
  5. what's that look like ? MOT · eng_USA_Brown/sarah058.cha, line 1201 · Sarah 3;04
  6. don't look at me . KEN · eng_USA_Brown/sarah058.cha, line 1400 · Sarah 3;04
  1. eng_USA_Brown/sarah058.cha, line 537 · Sarah, 3;4.16
    MOTthat's paint .
    MOTyou in the paint .
    MOT*MOT: look at your hair .
    MOTyour hair looks like somethin(g) I could take off and stick in the washin(g) machine .
    MOT<would that> [//] # should I stick your head in the washin(g) machine ?
  2. eng_USA_Brown/sarah058.cha, line 549 · Sarah, 3;4.16
    MOT<would that> [//] # should I stick your head in the washin(g) machine ?
    CHIno .
    KEN*KEN: look it .
    KENya got it all gooey .
    KENya got paint all up here .
  3. eng_USA_Brown/sarah058.cha, line 559 · Sarah, 3;4.16
    KENya got baby powder all up here .
    KENya can't even get it out .
    KEN*KEN: look .
    KENya still go like that to your hair (a)n(d) it's still comin(g) out .
    KENwhere's the rouge ?
  4. eng_USA_Brown/sarah058.cha, line 572 · Sarah, 3;4.16
    KENsomewhere ?
    KENya all stuck together .
    KEN*KEN: look like a rag mop .
    MOTMillisandy # Millisandy .
    KENhow's Chantilly been this week ?
  5. eng_USA_Brown/sarah058.cha, line 1201 · Sarah, 3;4.16
    MOTbath+room kitchen .
    KENwhat's a bath+room kitchen # like ?
    MOT*MOT: what's that look like ?
    CHIwhat ?
    MOTa bath+room kitchen .
  6. eng_USA_Brown/sarah058.cha, line 1400 · Sarah, 3;4.16
    CHICandy_Fashion .
    MOTuh # Baby # uh # Bright # Bridie Bright +...
    KEN*KEN: don't look at me .
    MOTno # Bonnie_Bride .
    MOTthat's it .

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_Brown3.02
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah058.chaSarah 3;4.161,986663.02

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. look6 · 100.0%