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Automatic Detection of Language and Annotation Model Information in CoNLL Corpora

Authors Frank Abromeit , Christian Chiarcos

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Subject Classification

ACM Subject Classification
  • Information systems → Structure and multilingual text search
Keywords
  • LLOD
  • CoNLL
  • OLiA

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Abstract

We introduce AnnoHub, an on-going effort to automatically complement existing language resources with metadata about the languages they cover and the annotation schemes (tagsets) that they apply, to provide a web interface for their curation and evaluation by means of domain experts, and to publish them as a RDF dataset and as part of the (Linguistic) Linked Open Data (LLOD) cloud. In this paper, we focus on tabular formats with tab-separated values (TSV), a de-facto standard for annotated corpora as popularized as part of the CoNLL Shared Tasks. By extension, other formats for which a converter to CoNLL and/or TSV formats does exist, can be processed analoguously. We describe our implementation and its evaluation against a sample of 93 corpora from the Universal Dependencies, v.2.3.

Cite As Get BibTex

Frank Abromeit and Christian Chiarcos. Automatic Detection of Language and Annotation Model Information in CoNLL Corpora. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 23:1-23:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.LDK.2019.23

Author Details

Frank Abromeit
  • Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany
Christian Chiarcos
  • Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany

Funding

The research described in this paper was conducted in the context of the Specialized Information Service Linguistics, funded by German Research Foundation (DFG/LIS, 2017-2019). The contributions of the second author were conducted with additional support from the Horizon 2020 Research and Innovation Action "Pret-a-LLOD. Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors" (H2020-ICT-2018-2, 2019-2021).

Supplementary Materials

References

  1. Christian Chiarcos and Christian Fäth. CoNLL-RDF: Linked corpora done in an NLP-friendly way. In Jorge Gracia, Francis Bond, John P. McCrae, Paul Buitelaar, Christian Chiarcos, and Sebastian Hellmann, editors, Language, Data, and Knowledge, pages 74-88, Cham, Switzerland, 2017. Springer. Google Scholar
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  3. Christian Chiarcos, Sebastian Nordhoff, and Sebastian Hellmann. Linked Data in Linguistics. Springer, 2012. Google Scholar
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