Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1296843.1296877 | ASSETS |
Keywords | Field | DocType |
aac user,resulting system,corpus study,in-domain data,ideally training,aac text,topic modeling,word prediction,large corpus,out-of-domain data,different text,approximate text,language modeling,language model,corpora | Computer science,Keystroke logging,Human–computer interaction,Artificial intelligence,Natural language processing,Topic model,Language model,Augmentative and alternative communication,Prediction system | Conference |
Citations | PageRank | References |
11 | 0.79 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keith Trnka | 1 | 97 | 7.51 |
| Kathleen F. McCoy | 2 | 671 | 93.90 |