Towards semantically rich and recursive word learning models

Francis Mollica, Steven T Piantadosi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Current models of word learning focus on the mapping between words and their referents and remain mute with regard to conceptual representation. We develop a cross-situational model of word learning that captures word-concept mapping by jointly inferring the referents and underlying concepts for each word. We also develop a variant of our model that incorporates recursion, which entertains the idea that children can use learned words to aid future learning. We demonstrate both models' ability to learn kinship terms and show that adding recursion into the model speeds acquisition.
Original languageEnglish
Title of host publicationProceedings of the 37th Annual Conference of the Cognitive Science Society 2015
PublisherAustin TX: Cognitive Science Society
Number of pages6
ISBN (Electronic)978-0-9911967-2-2
ISBN (Print)9781510809550
Publication statusPublished - 25 Jul 2015
EventCogSci 2015 - Pasadena, United States
Duration: 23 Jul 201525 Jul 2015


ConferenceCogSci 2015
Country/TerritoryUnited States

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