Corpus studies in word prediction
Keith Trnka, Kathleen F. McCoy · 2007
Word prediction can be used to enhance the communication rate of people with disabilities who use Augmentative and Alternative Communication (AAC) devices. We use statistical methods in a word prediction system, which are trained on a corpus, and then measure the efficacy of the resulting system by calculating the theoretical keystroke savings on some held out data. Ideally training and testing should be done on a large corpus of AAC text covering a variety of topics, but no such corpus exists. We discuss training and testing on a wide variety of corpora meant to approximate text from AAC users. We show that training on a combination of in-domain data with out-of-domain data is often more beneficial than either data set alone and that advanced language modeling such as topic modeling is portable even when applied to very different text.