PHPCLANCHILDES with regular expressions

5 matches in 5 utterances

3.08 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (375 utterances, 1,625 words searched).

  1. Pierre # Le Duc . MOT · eng_USA_Brown/sarah092.cha, line 317 · Sarah 4;01
  2. Pierre # not Pierre # Le Duc . ANN · eng_USA_Brown/sarah092.cha, line 320 · Sarah 4;01
  3. Pierre # Le Duc . MOT · eng_USA_Brown/sarah092.cha, line 322 · Sarah 4;01
  4. Pierre Le Duc ? ANN · eng_USA_Brown/sarah092.cha, line 326 · Sarah 4;01
  5. Pierre, Le Duc . MOT · eng_USA_Brown/sarah092.cha, line 328 · Sarah 4;01
  1. MELa new poodle .
    MOTyeah # she got a poodle the other day .
    MOT*MOT: Pierre # Le Duc .
    ANNPierre # not Pierre # Le Duc .
    MOTPierre # Le Duc .
  2. MOTyeah # she got a poodle the other day .
    MOTPierre # Le Duc .
    ANN*ANN: Pierre # not Pierre # Le Duc .
    MOTPierre # Le Duc .
    MOTthat's his whole name .
  3. MOTPierre # Le Duc .
    ANNPierre # not Pierre # Le Duc .
    MOT*MOT: Pierre # Le Duc .
    MOTthat's his whole name .
    ANNPierre Le Duc ?
  4. MOTPierre # Le Duc .
    MOTthat's his whole name .
    ANN*ANN: Pierre Le Duc ?
    MOTPierre, Le Duc .
    ANNoh .
  5. MOTthat's his whole name .
    ANNPierre Le Duc ?
    MOT*MOT: Pierre, Le Duc .
    ANNoh .
    CHII stick a bead .

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_Brown3.08
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
eng_USA_Brown/sarah092.chaSarah 4;1.181,625553.08

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. duc5 · 100.0%