PHPCLANCHILDES with regular expressions

17 matches in 16 utterances

5.88 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (606 utterances, 2,889 words searched).

  1. <was the ice> [/?] what was the ice like? MOT · autistic-flusberg/brett07.cha, line 1355
  2. <was the ice> [/?] what was the ice like? MOT · autistic-flusberg/brett07.cha, line 1355
  3. was it like a little teeny bit of ice? MOT · autistic-flusberg/brett07.cha, line 1366
  4. what kind of ice? MOT · autistic-flusberg/brett07.cha, line 1370
  5. what else des it do to the ice? MOT · autistic-flusberg/brett07.cha, line 1387
  6. cleans the ice. MOT · autistic-flusberg/brett07.cha, line 1395
  7. by driveing the machine on the ice. MOT · autistic-flusberg/brett07.cha, line 1458
  8. why did they drive the machine on the ice? MOT · autistic-flusberg/brett07.cha, line 1470
  9. look, why did they drive the machine on the ice? MOT · autistic-flusberg/brett07.cha, line 1473
  10. well, what do you think will happen if the ice gets dirty? MOT · autistic-flusberg/brett07.cha, line 1479
  11. how did those people clean that ice? MOT · autistic-flusberg/brett07.cha, line 1488
  12. what do the people use to clean the ice? MOT · autistic-flusberg/brett07.cha, line 1490
  13. when do they clean the ice? MOT · autistic-flusberg/brett07.cha, line 1493
  14. when the ice gets +/. MOT · autistic-flusberg/brett07.cha, line 1494
  15. <when, w, h, e, n> [/?] when do the people clean the ice? MOT · autistic-flusberg/brett07.cha, line 1498
  16. then how des the ice look +/. MOT · autistic-flusberg/brett07.cha, line 1503
  17. how des the ice look after they clean it? MOT · autistic-flusberg/brett07.cha, line 1505
  1. CHIthe ice.
    MOTyeah.
    MOT*MOT: <was the ice> [/?] what was the ice like?
    CHIwater.
    MOTwhat kind of water?
  2. MOTyeah.
    MOT<and was it> [/?] what else was it?
    MOT*MOT: was it like a little teeny bit of ice?
    CHIno.
    MOTno.
  3. CHIno.
    MOTno.
    MOT*MOT: what kind of ice?
    CHIbig ice.
    MOTwas it really huge?
  4. MOTtell me about the machine.
    CHIthe machine made the ice.
    MOT*MOT: what else des it do to the ice?
    CHImakes it.
    MOTit makes it and it +/.
  5. MOT<cl> [/?] +/.
    CHIclean.
    MOT*MOT: cleans the ice.
    CHIhow about if the ice gets dirty?
    MOTtell me about the machine.
  6. MOTby <dri> [/?] +/.
    CHIdriving.
    MOT*MOT: by driveing the machine on the ice.
    MOTwhy was there a <mist> [/?] steering wheel?
    CHIbecause they're driving the machine.
  7. MOT<hey> [/?] look at me.
    MOTthankyou.
    MOT*MOT: why did they drive the machine on the ice?
    CHI<because> [/?] because the machine made it.
    MOTno.
  8. CHI<because> [/?] because the machine made it.
    MOTno.
    MOT*MOT: look, why did they drive the machine on the ice?
    CHIbecause they +/.
    MOTso they can do what?
  9. MOTyeah.
    CHIhow about if the ice gets dirty?
    MOT*MOT: well, what do you think will happen if the ice gets dirty?
    CHIget clean.
    MOTit will get clean.
  10. MOTit will be clean why?
    MOTI mean how?
    MOT*MOT: how did those people clean that ice?
    CHI<so the> [/?] because the ice won't get dirty.
    MOTwhat do the people use to clean the ice?
  11. MOThow did those people clean that ice?
    CHI<so the> [/?] because the ice won't get dirty.
    MOT*MOT: what do the people use to clean the ice?
    CHIthe machine.
    MOTyeah.
  12. CHIthe machine.
    MOTyeah.
    MOT*MOT: when do they clean the ice?
    MOTwhen the ice gets +/.
    CHIdirty.
  13. MOTyeah.
    MOTwhen do they clean the ice?
    MOT*MOT: when the ice gets +/.
    CHIdirty.
    MOTyeah.
  14. MOTyeah.
    MOT<hey> [/?] brett.
    MOT*MOT: <when, w, h, e, n> [/?] when do the people clean the ice?
    MOT<af> [/?] +/.
    CHIafter it gets dirty.
  15. MOTgood brett.
    MOTgood brett.
    MOT*MOT: then how des the ice look +/.
    MOTlookit.
    MOThow des the ice look after they clean it?
  16. MOTthen how des the ice look +/.
    MOTlookit.
    MOT*MOT: how des the ice look after they clean it?
    CHInice.

Matches by child

Matches per 1,000 words, all transcripts of each child together, highest first.

  1. Brett autistic-flusberg5.88
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
autistic-flusberg/brett07.chaBrett 2,88916175.88

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. ice17 · 100.0%