PHPCLANCHILDES with regular expressions

3 matches in 3 utterances

4.03 per 1,000 words of everyone but CHI, in 1 of 1 transcripts (163 utterances, 744 words searched).

  1. on va l(e) ranger . 123153_126063 GER · french/lyon_mar06b.cha, line 44 · Marie 1;02
  2. on va le ranger . MOT · french/lyon_mar06b.cha, line 49 · Marie 1;02
  3. on va l(e) ranger . MOT · french/lyon_mar06b.cha, line 53 · Marie 1;02
  1. french/lyon_mar06b.cha, line 44 · Marie, 1;2.27
    GERparc(e) que je sens qu'elle va se faire mal . 121071_123153
    GERallez hop .
    GER*GER: on va l(e) ranger . 123153_126063
    MOT+< on va jouer à autre chose . 126063_129104
    MOTon va le ranger .
  2. french/lyon_mar06b.cha, line 49 · Marie, 1;2.27
    GERon va l(e) ranger . 123153_126063
    MOT+< on va jouer à autre chose . 126063_129104
    MOT*MOT: on va le ranger .
    MOTtu m(e) donnes ?
    MOTon va l(e) ranger .
  3. french/lyon_mar06b.cha, line 53 · Marie, 1;2.27
    MOTon va le ranger .
    MOTtu m(e) donnes ?
    MOT*MOT: on va l(e) ranger .
    MOTallez Mar allez .
    MOTregardes le livre que tu viens de recevoir .

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. Marie french4.03
All transcripts as a table
TranscriptChildAgeWordsUtterances matchedMatchesPer 1,000 words
french/lyon_mar06b.chaMarie 1;2.27744334.03

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. ranger3 · 100.0%