Doctoral Dissertation Research: Empirical Studies and Probabilistic Models of Word Segmentation and Word Learning

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

How do children learn their first words? To learn even a simple word like "table," a child must first pick out that word, rather than, for example, the non-word "the-tay," from the continuous string of syllables comprising a sentence like "it's under the table." The child must also learn that the word "table" refers to one particular object (a table), rather than any of the other things present at the time the word was used (a chair, the family dog, etc.). Recent research suggests that children make use of the statistical distribution of phonemes to solve first problem, word segmentation, and similarly make use of the co-occurrence statistics of words and objects to solve the second problem, word-world mapping. Graduate student Michael Frank, under the guidance of Dr. Edward Gibson, will use computational methods to help to characterize the nature of the learning mechanisms involved in these tasks. This grant will support studies with adults at MIT and a collaboration with Dr. Anne Fernald at Stanford University to conduct experiments with infants and young children. The goal of these experiments is to vary natural parameters in the learning situation using simple artificial languages to find out what makes learning harder or easier. Computational models will be evaluated on their fit to these data. This project will both contribute to our understanding of how children acquire the first words of their language and provide new directions for computer scientists attempting to create natural language processing systems.