Summary of the paper

Title GATEtoGerManC: A GATE-based Annotation Pipeline for Historical German
Authors Silke Scheible, Richard J. Whitt, Martin Durrell and Paul Bennett
Abstract We describe a new GATE-based linguistic annotation pipeline for Early Modern German, which can be used to annotate historical texts with word tokens, sentence boundaries, lemmas, and POS tags. The pipeline is based on a customisation of the freely available ANNIE system for English (Cunningham et al., 2002), in combination with a version of the TreeTagger (Schmid, 1994) trained on gold standard Early Modern German data. The POS-tagging and lemmatisation components of the pipeline achieve an average accuracy of 89.44% and 83.16%, respectively, on unseen historical data from various genres and publication dates within the Early Modern period. We show that normalisation of spelling variation can further improve these results. With no specialised tools available for processing this particular stage of the language, this pipeline will be of particular interest to smaller, humanities-based projects wishing to add linguistic annotations to their historical data but which lack the means or resources to develop such tools themselves.
Topics Corpus (creation, annotation, etc.), LR Infrastructures and Architectures, Standards for LRs
Full paper GATEtoGerManC: A GATE-based Annotation Pipeline for Historical German
Bibtex @InProceedings{SCHEIBLE12.978,
  author = {Silke Scheible and Richard J. Whitt and Martin Durrell and Paul Bennett},
  title = {GATEtoGerManC: A GATE-based Annotation Pipeline for Historical German},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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