Mission statement

The Journal for Language Technology and Computational Linguistics (JLCL) is an open access journal ("gold" OA) supported by the German Society for Computational Linguistics and Language Technology (GSCL). As such it requires no article processings charges (APC).


The journal publishes articles on computational linguistics and natural language processing, primarily original research papers and reports. If they are properly motivated surveys, position papers and book reviews may also be accepted. The following list of topics gives an overview of typical research fields relevant to the JLCL community:

  • Core technologies
    • Computational morphology, parsing and tagging (theory and technology)
    • Machine translation and machine aided translation
    • Speech based systems and their applications
  • Quantitative and data-oriented approaches
    • Corpus linguistics
    • Quantitative linguistics
  • Application fields
    • Information retrieval, knowledge management and related areas
    • Text technology (markup languages, text mining)
    • Computational lexicography
  • Inter-/Transdisciplinary work
    • CL/NLP for Digital Humanities
    • Linguistic approaches to the semantic Web and ontology engineering
    • Information Science and digital libraries
    • Diachronic and comparative linguistics
  • Curriculum development and teaching

We invite submissions related to any language while particularly encouraging submissions about German and its geographical and historical varieties.


Mostly two issues per annum: one general and open-ended call, the other in response to a specific call with a thematic focus.

JLCL is currently indexed in publons, dblp, and Google Scholar. The journal is also listed on the ACL Wiki. Further indexing efforts are under way.

Most cited articles as of March 2020:

  • Biemann, C. et al. (2013). Scalable Construction of High-Quality Web Corpora. JLCL, 28(2), 23-59. (68 times)
  • Chanier, T. et al. (2014). The CoMeRe corpus for French: structuring and annotating heterogeneous CMC genres. JLCL, 29(2), 1-30. (61 times)
  • Riedl, M., & Biemann, C. (2012). Text segmentation with topic models. JLCL, 27(1), 47-69. (67 times)

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