Circuits of the mind
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Unsupervised language acquisition
Unsupervised language acquisition
Sphinx-4: a flexible open source framework for speech recognition
Sphinx-4: a flexible open source framework for speech recognition
The oz of wizard: simulating the human for interaction research
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Touch and toys: new techniques for interaction with a remote group of robots
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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We present a method of grounded word learning that can learn the meanings of first and second person pronouns. The model selectively associates new words with agents in the environment by using already understood words to establish context. The method uses chi-square tests to find significant associations between the new words and attributes of the relevant agents. We show that this model can learn from a transcript of a parent-child interaction that "I" refers to the person who is speaking. With the additional information that questions about wants refer to the person being asked about them, the system learns that "you" refers to the person being addressed. We show that an incorrect assumption about the subject of "want" questions can lead to pronoun reversal, a linguistic error most commonly found in autistic and congenitally blind children. Finally, we present results from a physical implementation on a robot that runs in real time.