Learning of new words is assisted by contextual  information. This context can come in  several forms, including observations in nonlinguistic  semantic domains, as well as the linguistic  context in which the new word was presented

Anthony Kim

Keith Bonawitz

Seth Tardiff

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An architecture for word learning using bidirectional multimodal structural alignment

Learning of new words is assisted by contex-tual information. This context can come in several forms, including observations in non-linguistic semantic domains, as well as the lin-guistic context in which the new word was pre-sented. We outline a general architecture for word learning, in which structural alignment coordinates this contextual information in or-der to restrict the possible interpretations of un-known words. We identify spatial relations as an applicable semantic domain, and describe a system-in-progress for implementing the gen-eral architecture using video sequences as our non-linguistic input. For example, when the complete system is presented with “The bird dove to the rock, ” with a video sequence of a bird flying from a tree to a rock, and with the meanings for all the words except the preposi-tion “to, ” the system will register the unknown “to ” with the corresponding aspect of the bird’s trajectory. 

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http://clair.si.umich.edu/clair/HLT-NAACL03/lwm/pdf/lwm001.pdf

An Architecture for Word Learning using Bidirectional Multimodal Structural Alignment

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