rollins_corpus: CHILDES/Rollins naturalistic word-learning corpus (Frank et...

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

CHILDES/Rollins naturalistic word-learning corpus (Frank et al. 2009)

Description

The corpus the intentional Bayesian model of Frank, Goodman & Tenenbaum (2009) was fit to: 619 mother-to-infant utterances from the Rollins corpus in CHILDES, each paired with the set of objects present in the scene (six toys rotated in groups). 416 word types, 22 object types.

Usage

rollins_corpus

Format

An object of class list of length 3.

Details

A list with:

data

an xslData object with the 619 training utterances (data$train$words[[t]] / data$train$objects[[t]] are character vectors). No accuracy or test – there is no human referent-selection data for this corpus.

gold

the gold-standard lexicon as list(words, objects) (34 word-object pairs), for scoring a learned matrix with get_fscore(), get_roc(), get_roc_max(), or get_tp().

reference

the citation string.

Not part of xsl_datasets (which is scored by SSE against a human accuracy vector these corpora don't have). Imported from the wurwur package (https://github.com/mcfrank/wurwur); see data-raw/add_wurwur_corpora.R.

Source

Frank, M. C., Goodman, N. D., & Tenenbaum, J. B. (2009). Using speakers' referential intentions to model early cross-situational word learning. Psychological Science, 20(5), 578-585. Corpus originally from Rollins (2003), CHILDES.

Examples

m <- suppressWarnings(
  xsl_run(baseline(), rollins_corpus$data)$fits[[1]]$matrix)
get_roc_max(m, gold_lexicon = rollins_corpus$gold)

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