Better vocabularies for assistive communication aids: connecting terms using semantic networks and untrained annotators

The difficulties of navigating vocabulary in an assistive communication device are exacerbated for individuals with lexical access disorders like those due to aphasia. We present the design and implementation of a vocabulary network based on WordNet, a resource that attempts to model human semantic memory, that enables users to find words easily. To correct for the sparsity of links among words, we augment WordNet with additional connections derived from human judgments of semantic similarity collected in an online experiment. We evaluate the resulting system, the visual vocabulary for aphasia (ViVA), and describe its potential to adapt to a user's profile and enable faster search and improved navigation.

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