Adapting Word Prediction to Subject Matter without Topic-Labeled Data
Keith Trnka · 2008 · Proceedings of the 10th International ACM SIGACCESS Conference on Computers and Accessibility (Assets '08)
This paper proposes a method for adapting word prediction systems to the current topic of discourse without requiring human-labeled topic categories in the training data. Word prediction is a key feature of Augmentative and Alternative Communication (AAC) devices, where it…
word prediction · AAC · language model · topic modeling · natural language processing