Eliana Colunga

Using network representations of the lexicon has expanded our understanding of vocabulary growth processes and vocabulary structure during early development. These models of vocabulary development have used multiple types of sources to create lexical representations. More recently, Weber and Colunga (2022) demonstrated that predictions of early vocabulary norms can be improved by using network representations based on a corpus incorporating language a young child might typically hear. The present work goes a step further by evaluating the accuracy of network representations for predicting individual children's word learning that are based on embeddings that are readily available or embeddings gathered from the same child language corpus. We predicted the specific words that individual children add to their vocabulary over time, using a longitudinal data set of 86 monolingual English-speaking toddler's changing vocabulary from 18 to 30 months of age. The toddler-based network predicted word learning more accurately than the off-the-shelf network. Further, there was an advantage for prediction methods that took into account the individual child's particular network structure rather than overall network connectivity. These results highlight the importance of tailoring representational and processing choices to the population of interest. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages. Having long been multilingual, the field of computational morphology is increasingly moving towards approaches suitable for languages with minimal or no annotated resources. First, we survey recent developments in computational morphology with a focus on low-resource languages. Second, we argue that the field is ready to tackle the logical next challenge: understanding a language's morphology from raw text alone. We perform an empirical study on a truly unsupervised version of the paradigm completion task and show that, while existing state-of-the-art models bridged by two newly proposed models we devise perform reasonably, there is still much room for improvement. The stakes are high: solving this task will increase the language coverage of morphological resources by a number of magnitudes.

Does knowing certain words help children learn other words? We hypothesized that knowledge of more general (more superordinate) words at time-1 would lead to faster vocabulary growth as measured through vocabulary checklists administered at later timepoints. We find that this is indeed the case. Children who have similar vocabularies at time-1, but differ in their productive knowledge of more general words such as "animal", "picture", and "get" go on to have different rates of word learning. Knowledge of more general words is associated with faster vocabulary growth, particularly of words semantically related to the superordinate terms they are reported to produce. This positive relationship between knowledge of more general words and word learning remains even when controlling for measures of verbal and nonverbal intelligence.

Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words play a larger role in predicting network growth as these young learners add new words to their lexicon. We also examine how the importance of these semantic and phonological representations changes during the course of development. We propose a novel and significant theoretical framework for network growth models of acquisition and test the ability of these models to predict what words a specific child is likely to learn approximately one month in the future. We find that which acquisition model best fits is influenced by the underlying network representation, the assumed process of growth, and the network centrality measure used to relate the cognitive underpinnings of acquisition to network growth. The joint importance of representation, process, and the contribution of individual words to the predictive accuracy of the network model highlights the complex and multifaceted nature of early acquisition, provides new tools, and suggests experimental hypotheses for studying lexical acquisition.