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. Categories and Subject Descriptors: I.2.1 [Applications and Expert Systems]: Natural language interfaces; I.2.7 [Natural Language Processing]: Language models General Terms: Algorithms, Experimentation

• Type: Conference
• Year: 2008
• Where: ASSETS
• Authors: Keith Trnka
• Keywords: Alternative Communication Devices, ASSETS 2008, Basic Prediction Systems, Emerging Technology, Predictions