Quantifying Lexical Ambiguity in Speech To and From English-Learning Children

Abstract

Because words have multiple meanings, language users must often choose appropriate meanings according to the context of use. How this potential ambiguity affects first language learning, especially word learning, is unknown. Here, we present the first large-scale study of how children are exposed to, and themselves use, ambiguous words in their actual language learning environments. We tag 180,000 words in two longitudinal child language corpora with word senses from WordNet, focusing between 9 and 51 months and limiting to words from a popular parental vocabulary report. We then compare the diversity of sense usage in adult speech around children to that observed in a sample of adult-directed language, as well as the diversity of sense usage in children's own productions. To accomplish this we use a Bayesian model-based estimate of sense entropy, a measure of diversity that takes into account uncertainty inherent in small sample sizes. This reveals that sense diversity in caregivers' speech to children is similar to that observed in a sample of adult-directed written material, and that children' use of nouns --- but not verbs --- is similarly diverse to that of adults. Finally, we show that sense entropy is a significant predictor of vocabulary development: children begin to produce words with a higher diversity of adult sense usage at later ages. We discuss the implications of our findings for theories of word learning.

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

“…The entropy of these vectors was calculated using the Simpson's entropy (Simpson, 1949) with the Lande's correction (Lande, 1996; see also Good, 1953) as a measure of dispersion of semantic information in the dimensions of the vectors. In the psychological literature, the entropy of representations has been used as a measure of uncertainty, being a relevant predictor of vocabulary development (Meylan, Mankewitz, Floyd, Rabagliati, & Srinivasan, 2021). In this line, the differences in entropy have been used as a measure of information gain comparing different states of a phenomenon from a developmental perspective (see different examples in the outstanding book of Shultz, 2003 about computational developmental psychology).…”

Comparing Children and Large Language Models in Word Sense Disambiguation: Insights and Challenges

“…Sentence context plays a significant role in sense disambiguation (e.g., Sophia [played in / twisted] a band). Children master word ambiguities in naturalistic conversations (Meylan et al, 2021), which raises a question about which sentence properties facilitate child word disambiguation (Cabiddu et al, 2022b;Hahn, Snedeker, & Rabagliati, 2015;Khanna & Boland, 2010;Rabagliati et al, 2013). Children should access cues at different linguistic levels to successfully disambiguate senses.…”

“…Here, we evaluated how properties of child sense processing could be captured using model representations formed from naturalistic childdirected speech. This choice is motivated by differences in how senses are assigned to words in children and adults, likely due to differences in word use in child and adult environments (Meylan, Mankewitz, Floyd, Rabagliati, & Srinivasan, 2021).…”

From doggy to dog: Developmental shifts in children’s use of register-specific words

“…We focus here on the issue of encountering multiple labels for the same referent in early word learning, but children also face the inverse problem-one label for many different referents (e.g., Casey et al, 2021;Meylan et al, 2021). We see these puzzles of word learning as interrelated-and given children's early success in contending with both sources of variability, as evidence that learning happens at multiple levels.…”