Wordform- and Class-based Prediction of the Components of German Nominal Compounds in an AAC System

Wordform- and class-based prediction of the components of German nominal compounds in an AAC system Marco Baroni Johannes Matiasek Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna, Austria {marco,john}@oefai.at Harald Trost Department of Medical Cybernetics and Artificial Intelligence, University of Vienna Freyung 6/2 A-1010 Vienna, Austria harald@ai.univie.ac.at Abstract In word prediction systems for augmentative and al- ternative 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 try- ing to predict them as a whole. The model is im- proved further by the use of class-based modifier- head bigrams constructed using semantic classes automatically extracted from a corpus. The eval- uation 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. 1 Introduction N-gram language modeling techniques have been successfully embedded in a number of natural lan- guage processing applications, including word pre- dictors for augmentative and alternative communi- cation (AAC). N-gram based techniques rely cru- cially on the assumption that the large majority of words to be predicted have also occurred in the cor- pus used to train the models. Productive word-formation by compounding in languages such as German, Dutch, the Scandina- vian languages and Greek, where compounds are commonly written as single orthographic words, is problematic for this assumption. Productive compounding implies that a sizeable number of new words will constantly be added to the language. Such words cannot, in principle, be contained in any already existing training corpus, no matter how large. Moreover, the training cor- pus itself is likely to contain a sizeable number of newly formed compounds that, as such, will have an extremely low frequency, causing data sparse- ness problems. New compounds, however, differ from other types of new/rare words in that, while they are rare, they can typically be decomposed into more com- mon smaller units (the words that were put together to form them). For example, in the corpus we an- alyzed, Abend ’evening’ and Sitzung ’session’, the two components of the German compound Abend- sitzung ’evening session’, are much more frequent words than the latter. Thus, a natural way to handle productively formed compounds is to treat them not as primitive units, but as the concatenation of their components. A model of this sort will be able to predict newly formed compounds that never occurred in the train- ing corpus, as long as they can be analyzed as the concatenation of constituents that did occur in the training corpus. Moreover, a model of this sort avoids the specific type of data sparseness problems caused by newly formed compounds in the training corpus, since it collects statistics based on their (typ- ically more frequent) components. Building upon previous work (Spies, 1995; Carter et …

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