This paper presents a study on if and how automatically extracted keywords can be used to improve text categorization. In summary we show that a higher performance -as measured by micro-averaged F-measure on a standard text categorization collection -is achieved when the full-text representation is combined with the automatically extracted keywords. The combination is obtained by giving higher weights to words in the full-texts that are also extracted as keywords. We also present results for experiments in which the keywords are the only input to the categorizer, either represented as unigrams or intact. Of these two experiments, the unigrams have the best performance, although neither performs as well as headlines only.

We present a multilingual evaluation of approaches for spelling normalisation of historical text based on data from five languages: English, German, Hungarian, Icelandic, and Swedish. Three different normalisation methods are evaluated: a simplistic filtering model, a Levenshteinbased approach, and a character-based statistical machine translation approach. The evaluation shows that the machine translation approach often gives the best results, but also that all approaches improve over the baseline and that no single method works best for all languages.

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The article presents a new language learner corpus for Swedish, SweLL, and the methodology from collection and pesudonymisation to protect personal information of learners to annotation adapted to second language learning. The main aim is to deliver a well-annotated corpus of essays written by second language learners of Swedish and make it available for research through a browsable environment. To that end, a new annotation tool and a new project management tool have been implemented, – both with the main purpose to ensure reliability and quality of the final corpus. In the article we discuss reasoning behind metadata selection, principles of gold corpus compilation and argue for separation of normalization from correction annotation.

European libraries and archives are filled with enciphered manuscripts from the early modern period. These include military and diplomatic correspondence, records of secret societies, private letters, and so on. Although they are enciphered with classical cryptographic algorithms, their contents are unavailable to working historians. We therefore attack the problem of automatically converting cipher manuscript images into plaintext. We develop unsupervised models for character segmentation, character-image clustering, and decipherment of cluster sequences. We experiment with both pipelined and joint models, and we give empirical results for multiple ciphers.