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International Conference on Language, Data and Knowledge

LDK 2017: Language, Data, and Knowledge pp 74–88Cite as

CoNLL-RDF: Linked Corpora Done in an NLP-Friendly Way

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10318))

Abstract

We introduce CoNLL-RDF, a direct rendering of the CoNLL format in RDF, accompanied by a formatter whose output mimicks CoNLL’s original TSV-style layout. CoNLL-RDF represents a middle ground that accounts for the needs of NLP specialists (easy to read, easy to parse, close to conventional representations), but that also facilitates LLOD integration by applying off-the-shelf Semantic Web technology to CoNLL corpora and annotations. The CoNLL-RDF infrastructure is published as open source. We also provide SPARQL update scripts for selected use cases as described in this paper.

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Notes

  1. 1.

    As summarized by Mark Johnson in his ACL-IJCLNP 2012 keynote on the future of computational linguistics, “[s]tandard data formats (...) I’m not sure these are important: if someone can use a parser, they can probably also write a Python wrapper” [12, slide 8].

  2. 2.

    http://www.signll.org/conll.

  3. 3.

    https://www.w3.org/TR/sparql11-overview.

  4. 4.

    http://ilk.uvt.nl/conll/.

  5. 5.

    http://conll.cemantix.org/2011/data.html.

  6. 6.

    For the sake of processability, we use only a minimal fragment of NIF. We do neither adopt its full semantic model nor its URI formation constraints; yet, it is possible to transform CoNLL-RDF to NIF using SPARQL update and to provide NIF-compliant URIs if information about the original spacing (which is not preserved in CoNLL) is provided externally.

  7. 7.

    https://github.com/UniversalDependencies/UD_German.

  8. 8.

    It should be noted that addressing elements in a ragged array requires great care, as it is not guaranteed that a given index exists for every sentence, e.g., in case of SRL annotations which differ in length per sentence. In this regard, hash maps are more permissive.

  9. 9.

    CoNLL-RDF provides a clear representation of annotation layers: CoNLL has been described as a ‘hybrid standoff format’ [11] in the sense that every column represents a self-contained annotation layer that refers to a common segmentation (tokens).

  10. 10.

    http://www.openannotation.org/, https://www.w3.org/annotation/.

  11. 11.

    http://purl.org/powla.

  12. 12.

    https://www.w3.org/TR/r2rml, https://www.w3.org/TR/rdb-direct-mapping.

  13. 13.

    https://www.w3.org/TR/csv2rdf/.

  14. 14.

    NLTK provides numerous corpus readers specialized for different CoNLL variants, cf. http://www.nltk.org/howto/corpus.html.

  15. 15.

    http://acoli.informatik.uni-frankfurt.de/resources.html.

References

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Acknowledgments

The research of Christian Chiarcos was supported by the BMBF-funded Research Group ‘Linked Open Dictionaries (LiODi)’ (2015–2020). The research of Christian Fäth was conducted in the context of DFG-funded projects ‘Virtuelle Fachbibliothek’ (2015–2016) and ‘Fachinformationsdienst Linguistik’ (2017–2019).

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Authors and Affiliations

  1. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos & Christian Fäth

Authors
  1. Christian Chiarcos
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  2. Christian Fäth
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Correspondence to Christian Chiarcos .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Jorge Gracia

  2. Nanyang Technological University, Singapore, Singapore

    Francis Bond

  3. Insight Centre for Data Analytics, National University of Ireland, Galway, Galway, Ireland

    John P. McCrae

  4. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Paul Buitelaar

  5. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos

  6. University of Leipzig, Leipzig, Germany

    Sebastian Hellmann

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Chiarcos, C., Fäth, C. (2017). CoNLL-RDF: Linked Corpora Done in an NLP-Friendly Way. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-59888-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59887-1

  • Online ISBN: 978-3-319-59888-8

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