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Sökning: WFRF:(Cap Fabienne)

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  • Cap, Fabienne, et al. (författare)
  • SWORD : Towards Cutting-Edge Swedish Word Processing
  • 2016
  • Ingår i: Proceedings of SLTC 2016.
  • Konferensbidrag (refereegranskat)abstract
    • Despite many years of research on Swedish language technology, there is still no well-documented standard for Swedish word processing covering the whole spectrum from low-level tokenization to morphological analysis and disambiguation. SWORD is a new initiative within the SWE-CLARIN consortium aiming to develop documented standards for Swedish word processing. In this paper, we report on a pilot study of Swedish tokenization, where we compare the output of six different tokenizers on four different text types. For one text type (Wikipedia articles), we also compare to the tokenization produced by six manual annotators.
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  • Tang, Gongbo, et al. (författare)
  • An evaluation of neural machine translation models on historical spelling normalization
  • 2018
  • Ingår i: Proceedings of the 27th International Conference on Computational Linguistics. ; , s. 1320-1331
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.
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  • Tiedemann, Jörg, et al. (författare)
  • Phrase-Based SMT for Finnish with More Data, Better Models and Alternative Alignment and Translation Tools
  • 2016
  • Ingår i: Proceedings of the First Conference on Machine Translation. ; , s. 391-398
  • Konferensbidrag (refereegranskat)abstract
    • This paper summarises the contributions of the teams at the University of Helsinki, Uppsala University and the University of Turku to the news translation tasks for translating from and to Finnish. Our models address the problem of treating morphology and data coverage in various ways. We introduce a new efficient tool for word alignment and discuss factorisations, gappy language models and reinflection techniques for generating proper Finnish output. The results demonstrate once again that training data is the most effective way to increase translation performance.
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