adapting word prediction to subject matter without topic-labeled data
Word prediction helps to increase communication rate when using Augmentative and Alternative Communication devices. Basic prediction systems offer topically inappropriate predictions for the context, thus we adapt the predictions to the topic of discourse. However, previous work has relied on texts that are grouped into topics by humans. In contrast, we avoid this restriction by treating each document as a topic. The results are comparable to human-labeled topics and also the method is applicable to unlabeled text.
word prediction language modeling statistical methods topic modeling