PHPCLANCHILDES with regular expressions

7 matches in 6 utterances

3.83 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (595 utterances, 1,830 words searched).

  1. shall we put your name badge on ? MOT · eng_british_Manchester_I/becky18a.cha, line 820 · Becky 2;06
  2. shall we take this off now then ? MOT · eng_british_Manchester_I/becky18a.cha, line 1474 · Becky 2;06
  3. we should write your name on that , shouldn't we ? MOT · eng_british_Manchester_I/becky18a.cha, line 1544 · Becky 2;06
  4. we should write your name on that , shouldn't we ? MOT · eng_british_Manchester_I/becky18a.cha, line 1544 · Becky 2;06
  5. shall we have some teeth ? MOT · eng_british_Manchester_I/becky18a.cha, line 1875 · Becky 2;06
  6. and we need some of the little blue circles for middles . MOT · eng_british_Manchester_I/becky18a.cha, line 1956 · Becky 2;06
  7. we need some +//. MOT · eng_british_Manchester_I/becky18a.cha, line 1980 · Becky 2;06
  1. MOTxxx .
    INVwww .
    MOT*MOT: shall we put your name badge on ?
    CHIyeah .
    MOTwhat [/] what are you named today ?
  2. MOTeither way .
    MOTthat's right .
    MOT*MOT: shall we take this off now then ?
    CHIyeah .
    CHIwhere's this go ?
  3. MOT<that's right> [<] .
    MOTuhhum .
    MOT*MOT: we should write your name on that , shouldn't we ?
    MOTbecause it says this doctor's case belongs to doctor # Dodson .
    CHII can't do it .
  4. CHIyes .
    MOTand some teeth .
    MOT*MOT: shall we have some teeth ?
    MOThave you got any teeth ?
    MOToh yes .
  5. MOTlet's use these now .
    CHIxxx .
    MOT*MOT: and we need some of the little blue circles for middles .
    CHII don't have some red ones .
    INVno .
  6. MOT<there's one> [<] .
    MOTnow .
    MOT*MOT: we need some +//.
    MOTyou'd better have a bow in your hair because you've got a bobble in , haven't you ?
    CHIyes .

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. Becky eng_british_Manchester_I3.83
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_british_Manchester_I/becky18a.chaBecky 2;6.051,830673.83

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. we7 · 100.0%