We present a corpus of Finnish news articles with a manually prepared named entity annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source. The corpus is available for research purposes. We present baseline experiments on the corpus using a rule-based and two deep learning systems on two, in-domain and out-of-domain, test sets.

This paper presents two systems for spelling correction formulated as a sequence labeling task. One of the systems is an unstructured classifier and the other one is structured. Both systems are implemented using weighted finite-state methods. The structured system delivers stateof-the-art results on the task of tweet normalization when compared with the recent AliSeTra system introduced by Eger et al. (2016) even though the system presented in the paper is simpler than AliSeTra because it does not include a model for input segmentation. In addition to experiments on tweet normalization, we present experiments on OCR post-processing using an Early Modern Finnish corpus of OCR processed newspaper text.

This paper presents experiments on Optical character recognition (OCR) of historical newspapers and journals published in Finland. The corpus has two main languages: Finnish and Swedish and is written in both Blackletter and Antiqua fonts. Here we experiment with how much training data is enough to train high accuracy models, and try to train a joint model for both languages and all fonts. So far we have not been successful in getting one best model for all, but it is promising that with the mixed model we get the best results on the Finnish test set with 95 % CAR, which clearly surpasses previous results on this data set. CCS CONCEPTS • Applied computing → Optical character recognition.

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