DOMAIN-SPECIFIC WORD PREDICTION FOR AUGMENTATIVE COMMUNICATION

Abstract

Many augmentative communication systems employ word prediction to help minimize the number of user actions needed to construct messages. Statistical prediction techniques rely upon a database (model) of word frequencies and inter-word correlations derived from a large text corpus. One potential means to improve prediction is to create a set of models derived from domain-specific corpora, dynamically switching to the model most appropriate for the current conversation. Using telephone transcripts to generate prediction models for 20 different topic domains, we have observed a clear benefit to including domain-specific models in an overall prediction scheme.