PHPCLANCHILDES with regular expressions

6 matches in 6 utterances

3.94 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (370 utterances, 1,524 words searched).

  1. what # how +/. GLO · eng_USA_Brown/sarah047.cha, line 515 · Sarah 3;02
  2. how many ? GLO · eng_USA_Brown/sarah047.cha, line 879 · Sarah 3;02
  3. well someday you'll learn how to start . GLO · eng_USA_Brown/sarah047.cha, line 894 · Sarah 3;02
  4. how many do I have ? GLO · eng_USA_Brown/sarah047.cha, line 902 · Sarah 3;02
  5. count them and see how many knives . GLO · eng_USA_Brown/sarah047.cha, line 920 · Sarah 3;02
  6. how many ? GLO · eng_USA_Brown/sarah047.cha, line 951 · Sarah 3;02
  1. eng_USA_Brown/sarah047.cha, line 515 · Sarah, 3;2.02
    GLOmmhm .
    CHIshe shakin(g) head .
    GLO*GLO: what # how +/.
    CHIstop shakin(g) head .
    GLOhey # hey # we don't do that .
  2. eng_USA_Brown/sarah047.cha, line 879 · Sarah, 3;2.02
    MOTI think maybe it's because you have two in one hand and one in the other .
    CHIglasses .
    GLO*GLO: how many ?
    CHItwo [/] # two .
    GLOcount them you're supposed to count .
  3. eng_USA_Brown/sarah047.cha, line 894 · Sarah, 3;2.02
    GLOyeah you're wrong .
    GLOyeah .
    GLO*GLO: well someday you'll learn how to start .
    GLOSarah # what do I have here ?
    CHIum # knifes [*] [: knives] .
  4. eng_USA_Brown/sarah047.cha, line 902 · Sarah, 3;2.02
    GLOSarah # what do I have here ?
    CHIum # knifes [*] [: knives] .
    GLO*GLO: how many do I have ?
    CHItwo # three four five # six +... [+ bch]
    GLOxxx .
  5. eng_USA_Brown/sarah047.cha, line 920 · Sarah, 3;2.02
    GLOtell me what do I have here ?
    MOTlet me go see who this is .
    GLO*GLO: count them and see how many knives .
    CHIthree four +... [+ bch]
    CHICour(t)ney .
  6. eng_USA_Brown/sarah047.cha, line 951 · Sarah, 3;2.02
    GLOwhat do I have here ?
    CHIsneakers .
    GLO*GLO: how many ?
    CHIthree .
    GLOwhat do I have here ?

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_Brown3.94
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah047.chaSarah 3;2.021,524663.94

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. how6 · 100.0%