fm_corpus: Frank, Tenenbaum & Fernald naturalistic word-learning corpus

In XSLmodels: Run and Optimize Cross-Situational Word Learning Models

Frank, Tenenbaum & Fernald naturalistic word-learning corpus

Description

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.

Usage

fm_corpus

Format

An object of class list of length 5.

Details

A list with:

data

an xslData object with the 4763 training utterances (character-vector words/objects per trial). No accuracy/test.

intents

a length-4763 list; intents[[t]] is the object(s) the speaker referred to on utterance t (character vector, empty for non-referential utterances). A per-utterance gold signal.

gold

a hand-curated gold lexicon as list(words, objects) (41 pairs), for get_fscore() / get_roc() / get_tp().

gold_variants

list(strict, permissive) – two auto-derived alternatives (39 and 116 pairs) from looser vs. tighter co-occurrence thresholds on the coded intentions.

reference

the 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.

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.

Examples

length(fm_corpus$data$train$words)
fm_corpus$intents[[1]]
fm_corpus$gold_variants$strict$words

XSLmodels documentation built on Oct. 18, 2026, 5:08 p.m.