Models of learning word meanings have generally assumed prior knowledge of the concepts to which the words refer. However, novel natural language text or discourse often presents both unknown concepts and words which refer to these concepts. Also, developmental data suggests that the learning of words and their concepts frequently occurs concurrently instead of concept learning proceeding word learning. This paper presents an integrated computational model for acquiring both word meanings and their underlying concepts concurrently. This model is implemented as a word learning component added to the GENESIS explanation-based learning schema acquisition system for narrative understanding. A detailed example is described in which GENESIS learns provisional definitions for the words "kidnap", "kidnapper", and "ransom" as well as a kidnapping schema from a single narrative.
ML ID: 208
Recent explanation-based learning (EBL) models in AI allow a computer program to learn a schema by analyzing a single example. For example, GENESIS is an EBL system which learns a plan schema from a single specific instance presented in a narrative. Previous learning models in both AI and psychology have required multiple examples. This paper presents experimental evidence that people can learn a plan schema from a single narrative and that the learned schema agrees with that predicted by EBL. This evidence suggests that GENESIS, originally constructed as a machine learning system, can be interpreted as a psychological model of learning a complex schema from a single example.
ML ID: 207