PHPCLANCHILDES with regular expressions

7 matches in 7 utterances

3.08 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (579 utterances, 2,272 words searched).

  1. so there . MOT · eng_british_Manchester_II/nic11b.cha, line 189 · Nicole 2;04
  2. so it's probably a road . INV · eng_british_Manchester_II/nic11b.cha, line 206 · Nicole 2;04
  3. I don't think so . MOT · eng_british_Manchester_II/nic11b.cha, line 498 · Nicole 2;04
  4. can you open the door so we can see you sulking please . MOT · eng_british_Manchester_II/nic11b.cha, line 1058 · Nicole 2;04
  5. open the door wide so we can see you sulking please . MOT · eng_british_Manchester_II/nic11b.cha, line 1063 · Nicole 2;04
  6. sit up so I can do your nose then . MOT · eng_british_Manchester_II/nic11b.cha, line 1409 · Nicole 2;04
  7. are you going to come and sit up on the settee please so I can do your nose ? MOT · eng_british_Manchester_II/nic11b.cha, line 1416 · Nicole 2;04
  1. CHIoh .
    MOTwanting to know if they could come and give us a free survey .
    MOT*MOT: so there .
    MOTwww .
    MOTwhat're you building again ?
  2. INVit was a house but we're [/] we're putting the car on it now , aren't we ?
    MOToh right .
    INV*INV: so it's probably a road .
    INVwww .
    CHIthat .
  3. MOTyeah .
    INVyes .
    MOT*MOT: I don't think so .
    MOTno .
    MOTthey don't come off that way .
  4. INVare you at the shops ?
    MOTcan we come and look at you sulking ?
    MOT*MOT: can you open the door so we can see you sulking please .
    MOThey ?
    MOTopen the door wide so we can see you sulking please .
  5. MOTcan you open the door so we can see you sulking please .
    MOThey ?
    MOT*MOT: open the door wide so we can see you sulking please .
    INVI can see Nicole .
    MOTmmhm .
  6. MOTdo you need some love ?
    MOTwell .
    MOT*MOT: sit up so I can do your nose then .
    CHIxxx .
    MOTxxx .
  7. CHIxxx .
    MOTxxx .
    MOT*MOT: are you going to come and sit up on the settee please so I can do your nose ?
    MOThmm ?
    MOTeverything's no , isn'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. Nicole eng_british_Manchester_II3.08
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_british_Manchester_II/nic11b.chaNicole 2;4.182,272773.08

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. so7 · 100.0%