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The computational linguistics community in The Netherlands and Belgium has long recognized the dire need for a major reference corpus of written Dutch. In part to answer this need, the STEVIN programme was established. To pave the way for the effective building of a 500-million-word reference corpus of written Dutch, a pilot project was established. The(More)
We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shallow , easy-to-compute features. We train the algorithms on both tasks individually and on the two tasks simultaneously. The parameters of both algorithms and the selection of features are optimized(More)
In The Low Countries, a major reference corpus for written Dutch is currently being built. In this paper, we discuss the interplay between data acquisition and data processing during the creation of the SoNaR Corpus. Based on recent developments in traditional corpus compiling and new web harvesting approaches, SoNaR is designed to contain 500 million(More)
Some time in the future, some spelling error correction system will correct all the errors, and only the errors. We need evaluation metrics that will tell us when this has been achieved and that can help guide us there. We survey the current practice in the form of the evaluation scheme of the latest major publication on spelling correction in a leading(More)
We explore the feasibility of using only unsupervised means to identify non-words, i.e. typos, in a frequency list derived from a large corpus of Dutch and to distinguish between these non-words and real-words in the language. We call the system we built and evaluate in this paper CICCL, which stands for 'Corpus-Induced Corpus Clean-up'. The algorithm on(More)
We describe results on pitch accent placement in Dutch text obtained with a memory-based learning approach. The training material consists of newspaper texts that have been prosodically annotated by humans, and subsequently enriched with linguistic features and informational metrics using generally available, low-cost, shallow, knowledge-poor tools. We(More)