Class-based n-gram models of natural language
Computational Linguistics
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ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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ACM Transactions on Accessible Computing (TACCESS)
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TextEntry '03 Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods
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ICCHP'06 Proceedings of the 10th international conference on Computers Helping People with Special Needs
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In word prediction systems for augmentative and alternative communication (AAC), productive word-formation processes such as compounding pose a serious problem. We present a model that predicts German nominal compounds by splitting them into their modifier and head components, instead of trying to predict them as a whole. The model is improved further by the use of class-based modifier-head bigrams constructed using semantic classes automatically extracted from a corpus. The evaluation shows that the split compound model with class bigrams leads to an improvement in keystroke savings of more than 15% over a no split compound baseline model. We also present preliminary results obtained with a word prediction model integrating compound and simple word prediction.