PHPCLANCHILDES with regular expressions

8 matches in 7 utterances

4.02 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (548 utterances, 1,992 words searched).

  1. did we go and see a castle , Ruth ? MOT · eng_british_Manchester_II/ruth33b.cha, line 128 · Ruth 2;11
  2. what did we see in the castle ? MOT · eng_british_Manchester_II/ruth33b.cha, line 140 · Ruth 2;11
  3. and <what did we see> [//] what else did we see in the castle ? MOT · eng_british_Manchester_II/ruth33b.cha, line 160 · Ruth 2;11
  4. and <what did we see> [//] what else did we see in the castle ? MOT · eng_british_Manchester_II/ruth33b.cha, line 160 · Ruth 2;11
  5. now tell Anna what [//] did we see any penguins ? MOT · eng_british_Manchester_II/ruth33b.cha, line 809 · Ruth 2;11
  6. where did we see penguins ? MOT · eng_british_Manchester_II/ruth33b.cha, line 822 · Ruth 2;11
  7. let me see what you're doing it on ? MOT · eng_british_Manchester_II/ruth33b.cha, line 1389 · Ruth 2;11
  8. oh I see . MOT · eng_british_Manchester_II/ruth33b.cha, line 1931 · Ruth 2;11
  1. CHI<with little with little> [/] with little walls .
    MOTlittle walls .
    MOT*MOT: did we go and see a castle , Ruth ?
    CHIyeah .
    CHIwith big walls .
  2. MOTwith big walls .
    CHII 0am [*] going build one with little walls .
    MOT*MOT: what did we see in the castle ?
    CHIdead bird .
    MOTa dead bird .
  3. MOTbecause .
    MOTyes .
    MOT*MOT: and <what did we see> [//] what else did we see in the castle ?
    CHIa swan .
    MOTa what darling ?
  4. MOTyeah .
    CHIput them there .
    MOT*MOT: now tell Anna what [//] did we see any penguins ?
    CHIyeah .
    MOTwhere ?
  5. MOTin the field .
    MOTno .
    MOT*MOT: where did we see penguins ?
    CHIin the park .
    MOTin the park ?
  6. MOTwhat're you doing with your fingers ?
    CHIin there .
    MOT*MOT: let me see what you're doing it on ?
    CHIjumper .
    MOTyour jumper .
  7. CHIwe 0are [*] not doing that .
    CHIdoing that .
    MOT*MOT: oh I see .
    MOTlike that .
    MOTrow row +/.

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. Ruth eng_british_Manchester_II4.02
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_british_Manchester_II/ruth33b.chaRuth 2;11.141,992784.02

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. see8 · 100.0%