PHPCLANCHILDES with regular expressions

7 matches in 6 utterances

5.62 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (303 utterances, 1,245 words searched).

  1. how'd you get hurt ? COU · eng_USA_Brown/sarah027.cha, line 1025 · Sarah 2;09
  2. how'd you cut your head ? MOT · eng_USA_Brown/sarah027.cha, line 1036 · Sarah 2;09
  3. <tell them all about> [//] <how'd> [/] <how'd> [//] tell them how you got your cut . MOT · eng_USA_Brown/sarah027.cha, line 1096 · Sarah 2;09
  4. <tell them all about> [//] <how'd> [/] <how'd> [//] tell them how you got your cut . MOT · eng_USA_Brown/sarah027.cha, line 1096 · Sarah 2;09
  5. how'd you do it ? MOT · eng_USA_Brown/sarah027.cha, line 1101 · Sarah 2;09
  6. how'd you do that ? MOT · eng_USA_Brown/sarah027.cha, line 1103 · Sarah 2;09
  7. how'd he fix your boo+boo ? COU · eng_USA_Brown/sarah027.cha, line 1159 · Sarah 2;09
  1. COUwhat's the bandaid for ?
    CHIa dis .
    COU*COU: how'd you get hurt ?
    CHIa dis .
    COUwhat happened ?
  2. CHII (do)n('t) know .
    MOTyou do too !
    MOT*MOT: how'd you cut your head ?
    MOTc(o)me (h)ere .
    CHII (do)n('t) know .
  3. GLOwhat did he say to you ?
    CHIhe know me xxx .
    MOT*MOT: <tell them all about> [//] <how'd> [/] <how'd> [//] tell them how you got your cut .
    CHIsee # I got my cut .
    MOThow'd you do it ?
  4. MOT<tell them all about> [//] <how'd> [/] <how'd> [//] tell them how you got your cut .
    CHIsee # I got my cut .
    MOT*MOT: how'd you do it ?
    MOThow'd you do that ?
    CHII (do)n('t) know .
  5. CHIsee # I got my cut .
    MOThow'd you do it ?
    MOT*MOT: how'd you do that ?
    CHII (do)n('t) know .
    CHIxxx .
  6. CHIa fixed my boo+boo all up .
    MOThe fixed your boo+boo all up .
    COU*COU: how'd he fix your boo+boo ?
    COUwhat'd he do to your boo+boo ?
    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_Brown5.62
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah027.chaSarah 2;9.1,245675.62

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. how'd7 · 100.0%