4763 caregiver utterances (24 mother-infant sessions), each paired with the objects present in the scene. 1122 word types, 30 object types present in scenes; roughly half the utterances are non-referential. Unlike rollins_corpus, the speaker's referential intention is hand-coded per utterance.
Source
Frank, M. C., Tenenbaum, J. B., & Fernald, A. (2013). Social and discourse contributions to the determination of reference in cross-situational word learning. Language Learning and Development, 9(1), 1-24.
Details
A list with:
dataan xslData object with the 4763 training utterances (character-vector
words/objectsper trial). Noaccuracy/test.intentsa length-4763 list;
intents[[t]]is the object(s) the speaker referred to on utterancet(character vector, empty for non-referential utterances). A per-utterance gold signal.golda hand-curated gold lexicon as
list(words, objects)(41 pairs), forget_fscore()/get_roc()/get_tp().gold_variantslist(strict, permissive)– two auto-derived alternatives (39 and 116 pairs) from looser vs. tighter co-occurrence thresholds on the coded intentions.referencethe citation string.
Not part of xsl_datasets. Gaze/hand attentional cues present in the source
CSVs are not imported. Imported from the wurwur package
(https://github.com/mcfrank/wurwur); see
data-raw/add_wurwur_corpora.R.
Examples
length(fm_corpus$data$train$words)
#> [1] 4763
fm_corpus$intents[[1]]
#> [1] "dog"
fm_corpus$gold_variants$strict$words
#> [1] "ball" "blocks" "book" "box" "lid" "brushes"
#> [7] "cars" "shawns" "car" "doors" "red" "opens"
#> [13] "wheel" "door" "fast" "cheese" "dog" "doggie"
#> [19] "puppy" "doggys" "dogs" "doggies" "doggy" "doll"
#> [25] "rosy" "hotdog" "mickey" "pigs" "piggys" "puppet"
#> [31] "piggie" "squeaker" "piggy" "pig" "squeak" "trucks"
#> [37] "truck" "teddy" "green"