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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 present a new approach based on anagram hashing to handle globally the lexical variation in large and noisy text collections. Lexical variation addressed by spelling correction systems is primarily typographical variation. This is typically handled in a local fashion: given one particular text string some system of retrieving near-neighbors is applied,(More)
In two concurrent projects in the Netherlands we are further developing TICCL or Text-Induced Corpus Clean-up. In project Nederlab TICCL is set to work on diachronic Dutch text. To this end it has been equipped with the largest diachronic lexicon and a historical name list developed at the Institute for Dutch Lexicology or INL. In project @PhilosTEI TICCL(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)
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)