PHPCLANCHILDES with regular expressions

15 matches in 15 utterances

6.39 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (603 utterances, 2,348 words searched).

  1. lots of paper here . MOT · eng_british_Manchester_I/anne33a.cha, line 197 · Anne 2;09
  2. what a lot of black pens I've got today . MOT · eng_british_Manchester_I/anne33a.cha, line 239 · Anne 2;09
  3. I think it looks like lots of money . MOT · eng_british_Manchester_I/anne33a.cha, line 331 · Anne 2;09
  4. lots of pennys . MOT · eng_british_Manchester_I/anne33a.cha, line 333 · Anne 2;09
  5. is it lots of pennys ? MOT · eng_british_Manchester_I/anne33a.cha, line 337 · Anne 2;09
  6. his shell has sort of scales on it like that . MOT · eng_british_Manchester_I/anne33a.cha, line 588 · Anne 2;09
  7. think I might need another piece of paper to do Mickey_Mouse . MOT · eng_british_Manchester_I/anne33a.cha, line 608 · Anne 2;09
  8. what else can I think of ? MOT · eng_british_Manchester_I/anne33a.cha, line 836 · Anne 2;09
  9. not very good at thinking of things ,, am I ? MOT · eng_british_Manchester_I/anne33a.cha, line 903 · Anne 2;09
  10. what a lot of things to do . MOT · eng_british_Manchester_I/anne33a.cha, line 1009 · Anne 2;09
  11. because I'd like a bowl of milk to drink . MOT · eng_british_Manchester_I/anne33a.cha, line 1273 · Anne 2;09
  12. what a lot of animals . MOT · eng_british_Manchester_I/anne33a.cha, line 1325 · Anne 2;09
  13. what a lot of animals . MOT · eng_british_Manchester_I/anne33a.cha, line 1329 · Anne 2;09
  14. what a lot of animals . MOT · eng_british_Manchester_I/anne33a.cha, line 1348 · Anne 2;09
  15. let's put a few of yours away . MOT · eng_british_Manchester_I/anne33a.cha, line 1557 · Anne 2;09
  1. MOTwhat do you think ?
    MOTthere we are .
    MOT*MOT: lots of paper here .
    MOToh .
    MOThave I gotta get you ?
  2. MOTtwo black ones .
    MOTthree black ones .
    MOT*MOT: what a lot of black pens I've got today .
    MOTright .
    MOTwhoops .
  3. MOTyou don't know ?
    MOTwell .
    MOT*MOT: I think it looks like lots of money .
    MOTlots of pennys .
    CHIyeah .
  4. MOTwell .
    MOTI think it looks like lots of money .
    MOT*MOT: lots of pennys .
    CHIyeah .
    MOTis it lots of pennys ?
  5. MOTlots of pennys .
    CHIyeah .
    MOT*MOT: is it lots of pennys ?
    CHIyeah .
    MOTor is it Father_Christmas's sacks ?
  6. MOTand an eye .
    MOTand then his [/] his shell doesn't have spots on it .
    MOT*MOT: his shell has sort of scales on it like that .
    MOTand he has little feet ,, doesn't he poking out .
    MOTlike that .
  7. MOTa Mickey_Mouse .
    MOToh flip .
    MOT*MOT: think I might need another piece of paper to do Mickey_Mouse .
    MOTshall I cheat ?
    CHIyes .
  8. MOTokay .
    MOTshall we +//.
    MOT*MOT: what else can I think of ?
    MOTshall we play Tummy_ache ?
    MOTwhere's the Tummy_ache game ?
  9. MOToh .
    MOTokay .
    MOT*MOT: not very good at thinking of things ,, am I ?
    MOTlook at all these poor toys in the cot .
    MOTthey're all a bit crowded in there ,, aren't they ?
  10. CHII did sticky and drawing and um [/] and playing .
    MOTsticky and drawing and playing ?
    MOT*MOT: what a lot of things to do .
    CHIand drawing .
    MOTand drawing .
  11. MOThave you got any milk at your shop ?
    CHIyeah .
    MOT*MOT: because I'd like a bowl of milk to drink .
    MOTis that my milk ?
    CHIyeah .
  12. MOTokay .
    MOToh .
    MOT*MOT: what a lot of animals .
    MOToh .
    MOTwhat a lot of animals .
  13. MOTwhat a lot of animals .
    MOToh .
    MOT*MOT: what a lot of animals .
    MOTthere's the Duplo horse .
    MOTand there's the pig .
  14. MOThmm ?
    CHIwhee !
    MOT*MOT: what a lot of animals .
    CHIoh no .
    CHIoh .
  15. CHIno .
    MOTwell .
    MOT*MOT: let's put a few of yours away .
    INVyeah .
    MOTand then there'll be some space ,, willn't 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. Anne eng_british_Manchester_I6.39
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_british_Manchester_I/anne33a.chaAnne 2;9.082,34815156.39

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.

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